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Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks
Published 22 Dec 2022 in cs.CV | (2212.11542v3)
Abstract: As a fundamental computer vision task, crowd counting plays an important role in public safety. Currently, deep learning based head detection is a promising method for crowd counting. However, the highly concerned object detection networks cannot be well applied to this problem for three reasons: (1) Existing loss functions fail to address sample imbalance in highly dense and complex scenes; (2) Canonical object detectors lack spatial coherence in loss calculation, disregarding the relationship between object location and background region; (3) Most of the head detection datasets are only annotated with the center points, i.e. without bounding boxes. To overcome these issues, we propose a novel Mask Focal Loss (MFL) based on heatmap via the Gaussian kernel. MFL provides a unifying framework for the loss functions based on both heatmap and binary feature map ground truths. Additionally, we introduce GTA_Head, a synthetic dataset with comprehensive annotations, for evaluation and comparison. Extensive experimental results demonstrate the superior performance of our MFL across various detectors and datasets, and it can reduce MAE and RMSE by up to 47.03% and 61.99%, respectively. Therefore, our work presents a strong foundation for advancing crowd counting methods based on density estimation.
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[2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. 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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. 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[2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, G., Gao, J., Liu, Q., Wang, Q., Wang, Y.: Cnn-based density estimation and crowd counting: A survey. arXiv preprint arXiv:2003.12783 (2020) Fekri-Ershad and Alsaffar [2023] Fekri-Ershad, S., Alsaffar, M.F.: Developing a tuned three-layer perceptron fed with trained deep convolutional neural networks for cervical cancer diagnosis. Diagnostics 13(4), 686 (2023) Tripathi et al. [2019] Tripathi, G., Singh, K., Vishwakarma, D.K.: Convolutional neural networks for crowd behaviour analysis: a survey. The Visual Computer 35(5), 753–776 (2019) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Eantrack: An efficient attention network for visual tracking. IEEE Transactions on Automation Science and Engineering (2023) Gu et al. [2022] Gu, F., Lu, J., Cai, C.: Rpformer: A robust parallel transformer for visual tracking in complex scenes. IEEE Transactions on Instrumentation and Measurement 71, 1–14 (2022) Yuan et al. [2023] Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Fekri-Ershad, S., Alsaffar, M.F.: Developing a tuned three-layer perceptron fed with trained deep convolutional neural networks for cervical cancer diagnosis. Diagnostics 13(4), 686 (2023) Tripathi et al. 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[2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. 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Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Tripathi, G., Singh, K., Vishwakarma, D.K.: Convolutional neural networks for crowd behaviour analysis: a survey. The Visual Computer 35(5), 753–776 (2019) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Eantrack: An efficient attention network for visual tracking. IEEE Transactions on Automation Science and Engineering (2023) Gu et al. [2022] Gu, F., Lu, J., Cai, C.: Rpformer: A robust parallel transformer for visual tracking in complex scenes. IEEE Transactions on Instrumentation and Measurement 71, 1–14 (2022) Yuan et al. [2023] Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. 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[2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. 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[2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gu, F., Lu, J., Cai, C.: Rpformer: A robust parallel transformer for visual tracking in complex scenes. IEEE Transactions on Instrumentation and Measurement 71, 1–14 (2022) Yuan et al. [2023] Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. 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[2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. 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[2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. 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In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. 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[2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Fekri-Ershad, S., Alsaffar, M.F.: Developing a tuned three-layer perceptron fed with trained deep convolutional neural networks for cervical cancer diagnosis. Diagnostics 13(4), 686 (2023) Tripathi et al. [2019] Tripathi, G., Singh, K., Vishwakarma, D.K.: Convolutional neural networks for crowd behaviour analysis: a survey. The Visual Computer 35(5), 753–776 (2019) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Eantrack: An efficient attention network for visual tracking. IEEE Transactions on Automation Science and Engineering (2023) Gu et al. [2022] Gu, F., Lu, J., Cai, C.: Rpformer: A robust parallel transformer for visual tracking in complex scenes. IEEE Transactions on Instrumentation and Measurement 71, 1–14 (2022) Yuan et al. [2023] Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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[2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. 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Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. 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In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Eantrack: An efficient attention network for visual tracking. IEEE Transactions on Automation Science and Engineering (2023) Gu et al. [2022] Gu, F., Lu, J., Cai, C.: Rpformer: A robust parallel transformer for visual tracking in complex scenes. IEEE Transactions on Instrumentation and Measurement 71, 1–14 (2022) Yuan et al. [2023] Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. 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[2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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[2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. 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[2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. 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In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. 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IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. 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[2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. 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In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. 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IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. 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IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. 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[2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yuan, D., Chang, X., Liu, Q., Yang, Y., Wang, D., Shu, M., He, Z., Shi, G.: Active learning for deep visual tracking. IEEE Transactions on Neural Networks and Learning Systems (2023) Gu et al. [2023] Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gu, F., Lu, J., Cai, C., Zhu, Q., Ju, Z.: Repformer: a robust shared-encoder dual-pipeline transformer for visual tracking. Neural Computing and Applications 35(28), 20581–20603 (2023) Sam et al. [2020] Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. 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[2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. 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In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. 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In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., Babu, R.V.: Locate, size, and count: Accurately resolving people in dense crowds via detection. IEEE transactions on pattern analysis and machine intelligence 43(8), 2739–2751 (2020) Song et al. [2021] Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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[2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. 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[2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. 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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. 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[2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Wu, Y.: Rethinking counting and localization in crowds: A purely point-based framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3365–3374 (2021) Wang et al. [2021] Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. 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[2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. 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IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, X., Chau, L.-P.: Dense point prediction: A simple baseline for crowd counting and localization. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2021). IEEE Zhou et al. [2019] Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019) Sundararaman et al. [2021] Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021) Hou et al. [2022] Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. 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IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Hou, Y., Li, C., Lu, Y., Zhu, L., Li, Y., Jia, H., Xie, X.: Enhancing and dissecting crowd counting by synthetic data. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2539–2543 (2022). IEEE Peng et al. [2018] Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. 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[2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. 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In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Peng, D., Sun, Z., Chen, Z., Cai, Z., Xie, L., Jin, L.: Detecting heads using feature refine net and cascaded multi-scale architecture. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2528–2533 (2018). IEEE Shao et al. [2018] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018) Wang et al. [2019] Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. 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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198–8207 (2019) Chen et al. [2012] Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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[2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: Bmvc, vol. 1, p. 3 (2012) Chen et al. [2013] Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. 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[2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. 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In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Pham et al. [2015] Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. 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[2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Pham, V.-Q., Kozakaya, T., Yamaguchi, O., Okada, R.: Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3253–3261 (2015) Walach and Wolf [2016] Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision, pp. 660–676 (2016). Springer Sindagi and Patel [2017a] Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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[2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1861–1870 (2017) Sindagi and Patel [2017b] Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017). IEEE Babu Sam et al. [2017] Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Li et al. [2018] Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Y., Zhang, X., Chen, D.: Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Liu et al. [2019] Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019) Cao et al. [2018] Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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[2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Bai et al. [2020] Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020) Song et al. [2021] Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. 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[2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Song, Q., Wang, C., Wang, Y., Tai, Y., Wang, C., Li, J., Wu, J., Ma, J.: To choose or to fuse? scale selection for crowd counting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2576–2583 (2021) Wu et al. [2006] Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. 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In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 214–219 (2006). IEEE An et al. [2007] An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007). IEEE Li et al. [2016] Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. 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Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, Z., Zhang, L., Fang, Y., Wang, J., Xu, H., Yin, B., Lu, H.: Deep people counting with faster r-cnn and correlation tracking. In: Proceedings of the International Conference on Internet Multimedia Computing and Service, pp. 57–60 (2016) Laradji et al. [2018] Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: Counting by localization with point supervision. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 547–562 (2018) Liu et al. [2019] Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, Y., Shi, M., Zhao, Q., Wang, X.: Point in, box out: Beyond counting persons in crowds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6469–6478 (2019) Wang et al. [2021] Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, Y., Hou, J., Hou, X., Chau, L.-P.: A self-training approach for point-supervised object detection and counting in crowds. IEEE Transactions on Image Processing 30, 2876–2887 (2021) Lian et al. [2021] Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. 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[2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lian, D., Chen, X., Li, J., Luo, W., Gao, S.: Locating and counting heads in crowds with a depth prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Chan and Vasconcelos [2009] Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 545–551 (2009). IEEE Idrees et al. [2013] Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. 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[2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Ryan et al. [2015] Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. 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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). 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[2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding 130, 1–17 (2015) Lempitsky and Zisserman [2010] Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in neural information processing systems 23 (2010) Gao et al. [2019] Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. 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IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). 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In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Gao, J., Wang, Q., Li, X.: Pcc net: Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology 30(10), 3486–3498 (2019) Xu et al. [2022] Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. 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IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. 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In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., Tomizuka, M.: Autoscale: Learning to scale for crowd counting. International Journal of Computer Vision 130(2), 405–434 (2022) Liu et al. [2020] Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
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In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Liu, W., Salzmann, M., Fua, P.: Estimating people flows to better count them in crowded scenes. In: European Conference on Computer Vision, pp. 723–740 (2020). Springer Zhang et al. [2021] Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021) Shu et al. [2022] Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Shu, W., Wan, J., Tan, K.C., Kwong, S., Chan, A.B.: Crowd counting in the frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19618–19627 (2022) Enzweiler and Gavrila [2008] Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE transactions on pattern analysis and machine intelligence 31(12), 2179–2195 (2008) Lin and Davis [2010] Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE transactions on pattern analysis and machine intelligence 32(4), 604–618 (2010) Dalal and Triggs [2005] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. 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Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005). Ieee Wu and Nevatia [2007] Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. 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[2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247–266 (2007) Subburaman et al. [2012] Subburaman, V.B., Descamps, A., Carincotte, C.: Counting people in the crowd using a generic head detector. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-based Surveillance, pp. 470–475 (2012). IEEE Zeng and Ma [2010] Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. 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[2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. 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[2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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[2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. 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- Zeng, C., Ma, H.: Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th International Conference on Pattern Recognition, pp. 2069–2072 (2010). IEEE Zhang et al. [2018] Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware r-cnn: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637–653 (2018) Ren et al. [2015] Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015) He et al. [2017] He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017) Lin et al. [2017] Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017) Chen et al. [2023] Chen, J., Wang, G., Liu, W., Zhong, X., Tian, Y., Wu, Z.: Perception reinforcement using auxiliary learning feature fusion: A modified yolov8 for head detection. arXiv preprint arXiv:2310.09492 (2023) Wang et al. [2019] Wang, J., Chen, K., Yang, S., Loy, C.C., Lin, D.: Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974 (2019) Law and Deng [2018] Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. 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Computerized Medical Imaging and Graphics 95, 102026 (2022) Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
- Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018) Tian et al. [2019] Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
- Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019) Kong et al. [2020] Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
- Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29, 7389–7398 (2020) Leng et al. [2022] Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
- Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., Anguelov, D.: Polyloss: A polynomial expansion perspective of classification loss functions. arXiv preprint arXiv:2204.12511 (2022) Li et al. [2020] Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
- Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33, 21002–21012 (2020) Yeung et al. [2022] Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022) Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
- Yeung, M., Sala, E., Schönlieb, C.-B., Rundo, L.: Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics 95, 102026 (2022)
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