Stampede Alert Clustering Algorithmic System Based on Tiny-Scale Strengthened DETR (2404.10359v1)
Abstract: A novel crowd stampede detection and prediction algorithm based on Deformable DETR is proposed to address the challenges of detecting a large number of small targets and target occlusion in crowded airport and train station environments. In terms of model design, the algorithm incorporates a multi-scale feature fusion module to enlarge the receptive field and enhance the detection capability of small targets. Furthermore, the deformable attention mechanism is improved to reduce missed detections and false alarms for critical targets. Additionally, a new algorithm is innovatively introduced for stampede event prediction and visualization. Experimental evaluations on the PKX-LHR dataset demonstrate that the enhanced algorithm achieves a 34% performance in small target detection accuracy while maintaining the original detection speed.
- X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” 2021.
- X. Kefan, Y. Song, S. Liu, and J. Liu, “Analysis of crowd stampede risk mechanism: A systems thinking perspective,” Kybernetes, vol. 48, no. 1, pp. 124–142, 2019.
- C. Wang, T. Wang, and Z. Zhou, “A dense small object detection method based on yolov4 for crowded scenes,” Applied Technology, 2024. [Online]. Available: https://link.cnki.net/urlid/23.1191.U.20240129.1659.002
- H. Yin, B. Chen, Y. Chai, and et al., “A review of vision-based object detection and tracking,” Acta Automatica Sinica, vol. 42, no. 10, pp. 1466–1489, 2016.
- M. Ahmed, K. A. Hashmi, A. Pagani, and et al., “Survey and performance analysis of deep learning based object detection in challenging environments,” Sensors, vol. 21, no. 15, p. 5116, 2021.
- N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” 2020.
- T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” 2017.
- L. Melas-Kyriazi, “Do you even need attention? a stack of feed-forward layers does surprisingly well on imagenet,” 2021.
- S. Liu, F. Li, H. Zhang, X. Yang, X. Qi, H. Su, J. Zhu, and L. Zhang, “Dab-detr: Dynamic anchor boxes are better queries for detr,” 2022.
- P. Gao, M. Zheng, X. Wang, J. Dai, and H. Li, “Fast convergence of detr with spatially modulated co-attention,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3601–3610.
- F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, “Dn-detr: Accelerate detr training by introducing query denoising,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 4, pp. 2239–2251, 2024.
- H. Hua and Q. Chen, “Hybrid kmeans with knn for network intrusion detection algorithm,” Computer Science, vol. 43, no. 03, pp. 158–162, 2016.
- L. Zhang, Y. Lu, Q. Hou, and H. Zhang, “Fuzzy clustering algorithm for k-nearest neighbors spatial density distribution,” Computer Science, vol. 50, no. 04, pp. 289–301, 2023.
- H. Hua, Q. Chen, H. Liu et al., “Hybrid kmeans with knn for network intrusion detection algorithm,” Computer Science, vol. 43, no. 03, pp. 158–162, 2016.
- S. Zahra, M. A. Ghazanfar, A. Khalid, M. A. Azam, U. Naeem, and A. Prugel-Bennett, “Novel centroid selection approaches for KMeans-clustering based recommender systems,” Information Sciences, vol. 320, pp. 156–189, 2015. [Online]. Available: https://doi.org/10.1016/j.ins.2015.03.062
- A. A. Abdulnassar and L. R. Nair, “Performance analysis of Kmeans with modified initial centroid selection algorithms and developed Kmeans9+ model,” Measurement: Sensors, vol. 25, p. 100666, 2023. [Online]. Available: https://doi.org/10.1016/j.measen.2023.100666
- X. Huang, “Intelligent warning and implementation of pedestrian crowded stampede events based on computer vision,” Ph.D. dissertation, Zhongnan University of Economics and Law, 2022.
- W. Chen, “Research on risk warning of crowd aggregation in urban public places based on deep learning,” Ph.D. dissertation, Northeast Normal University, 2023.