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Perspective Aware Road Obstacle Detection
Published 4 Oct 2022 in cs.CV and cs.RO | (2210.01779v2)
Abstract: While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this paper, we account for this by computing a scale map encoding the apparent size of a hypothetical object at every image location. We then leverage this perspective map to (i) generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening; and (ii) incorporate perspective information in the decoding part of the detection network to guide the obstacle detector. Our results on standard benchmarks show that, together, these two strategies significantly boost the obstacle detection performance, allowing our approach to consistently outperform state-of-the-art methods in terms of instance-level obstacle detection.
- “AutoNation Drive Editors’ Guide to Lane Assist Systems,” https://www.autonationdrive.com/research/best-cars-with-lane-assist.htm, 2021.
- K. Lis, K. Nakka, M. Salzmann, and P. Fua, “Detecting the Unexpected via Image Resynthesis,” in International Conference on Computer Vision, 2019.
- G. Di Biase, H. Blum, R. Siegwart, and C. Cadena, “Pixel-Wise Anomaly Detection in Complex Driving Scenes,” in Conference on Computer Vision and Pattern Recognition, June 2021.
- K. Lis, S. Honari, P. Fua, and M. Salzmann, “Detecting Road Obstacles by Erasing Them,” in arXiv Preprint, 2020.
- R. Chan, M. Rottmann, and H. Gottschalk, “Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation,” in International Conference on Computer Vision, 2021.
- P. Bevandić, I. Kreso, M. Orsic, and S. Segvić, “Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift,” in German Conference on Pattern Recognition, 2019.
- T. Vojir, T. Šipka, R. Aljundi, N. Chumerin, D. O. Reino, and J. Matas, “Road Anomaly Detection by Partial Image Reconstruction with Segmentation Coupling,” in International Conference on Computer Vision, October 2021.
- R. Chan, K. Lis, S. Uhlemeyer, H. Blum, S. Honari, R. Siegwart, P. Fua, M. Salzmann, and M. Rottmann, “Segmentmeifyoucan: A Benchmark for Anomaly Segmentation,” in Advances in Neural Information Processing Systems, 2021.
- P. Pinggera, S. Ramos, S. Gehrig, U. Franke, C. Rother, and R. Mester, “Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles,” in International Conference on Intelligent Robots and Systems, 2016.
- M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes Dataset for Semantic Urban Scene Understanding,” in Conference on Computer Vision and Pattern Recognition, 2016.
- M. Grcic, P. Bevandic, and S. Segvic, “DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition,” in European Conference on Computer Vision, 2022.
- R. Hadsell, P. Sermanet, J. Ben, A. Erkan, J. Han, B. Flepp, U. Muller, and Y. LeCun, “Online Learning for Offroad Robots: Using Spatial Label Propagation to Learn Long-Range Traversability,” in Robotics: Science and Systems Conference, vol. 11, 2007, p. 32.
- R. Hadsell, P. Sermanet, J. Ben, A. Erkan, M. Scoffier, K. Kavukcuoglu, U. Muller, and Y. LeCun, “Learning Long-Range Vision for Autonomous Off-Road Driving,” Journal of Field Robotics, vol. 26, no. 2, pp. 120–144, 2009.
- M. Liu, W. Buntine, and G. Haffari, “Learning How to Actively Learn: A Deep Imitation Learning Approach,” in Annual Meeting of the Association for Computational Linguistics, 2018.
- D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep Image Prior,” in Conference on Computer Vision and Pattern Recognition, 2018, pp. 9446–9454.
- Y. Lyu and X. Huang, “Road Segmentation Using CNN with GRU,” in arXiv Preprint, 2018.
- S. Choi, J. T. Kim, and J. Choo, “Cars Can’t Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks,” in Conference on Computer Vision and Pattern Recognition, 2020.
- X. Li, Z. Jie, W. Wang, C. Liu, J. Yang, X. Shen, Z. Lin, Q. Chen, S. Yan, and J. Feng, “Foveanet: Perspective-Aware Urban Scene Parsing,” in Conference on Computer Vision and Pattern Recognition, 2017.
- C. Huynh, A. Tran, K. Luu, and M. Hoai, “Progressive Semantic Segmentation,” in Conference on Computer Vision and Pattern Recognition, 2021.
- S. Bai, Z. He, Y. Lei, W. Wu, C. Zhu, M. Sun, and J. Yan, “Traffic Anomaly Detection via Perspective Map Based on Spatial-Temporal Information Matrix,” in Conference on Computer Vision and Pattern Recognition, 2019.
- C. D. Prakash, F. Akhbari, and L. J. Karam, “Robust Obstacle Detection for Advanced Driver Assistance Systems Using Distortions of Inverse Perspective Mapping of a Monocular Camera,” Robotics and Autonomous Systems, vol. 114, pp. 172–186, 2019.
- A. Chan, Z. Liang, and N. Vasconcelos, “Privacy Preserving Crowd Monitoring: Counting People Without People Models or Tracking,” in Conference on Computer Vision and Pattern Recognition, 2008.
- M. Shi, Z. Yang, C. Xu, and Q. Chen, “Revisiting Perspective Information for Efficient Crowd Counting,” in Conference on Computer Vision and Pattern Recognition, 2019.
- C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-Scene Crowd Counting via Deep Convolutional Neural Networks,” in Conference on Computer Vision and Pattern Recognition, 2015, pp. 833–841.
- Y. Yang, G. Li, Z. Wu, L. Su, Q. Huang, and N. Sebe, “Reverse Perspective Network for Perspective-Aware Object Counting,” in Conference on Computer Vision and Pattern Recognition, 2020.
- W. Liu, M. Salzmann, and P. Fua, “Context-Aware Crowd Counting,” in Conference on Computer Vision and Pattern Recognition, 2019.
- W. Liu, K. Lis, M. Salzmann, and P. Fua, “Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation,” International Conference on Intelligent Robots and Systems, 2019.
- S. Ramos, S. Gehrig, P. Pinggera, U. Franke, and C. Rother, “Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling,” in IEEE Intelligent Vehicles Symposium, 2017.
- K. Gupta, S. A. Javed, V. Gandhi, and K. M. Krishna, “MergeNet: A Deep Net Architecture for Small Obstacle Discovery,” in International Conference on Robotics and Automation, 2018.
- L. Sun, K. Yang, X. Hu, W. Hu, and K. Wang, “Real-time fusion network for RGB-D semantic segmentation incorporating unexpected obstacle detection for road-driving images,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5558–5565, 2020.
- K. Chaudhury, S. Diverdi, and S. Ioffe, “Auto-Rectification of User Photos,” in International Conference on Image Processing, 2014.
- S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” in Conference on Computer Vision and Pattern Recognition, 2017.
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A Large-Scale Hierarchical Image Database,” in Conference on Computer Vision and Pattern Recognition, 2009.
- S. Liang, Y. Li, and R. Srikant, “Enhancing the Reliability of Out-Of-Distribution Image Detection in Neural Networks,” in International Conference on Learning Representations, 2018.
- D. Hendrycks and K. Gimpel, “A Baseline for Detecting Misclassified and Out-Of-Distribution Examples in Neural Networks,” in International Conference on Learning Representations, 2017.
- H. Blum, P.-E. Sarlin, J. Nieto, R. Siegwart, and C. Cadena, “Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving,” in International Conference on Computer Vision, October 2019.
- K. Lee, K. Lee, H. Lee, and J. Shin, “A Simple Unified Framework for Detecting Out-Of-Distribution Samples and Adversarial Attacks,” in Advances in Neural Information Processing Systems, 2018, pp. 7167–7177.
- B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles,” in Advances in Neural Information Processing Systems, 2017.
- J. Mukhoti and Y. Gal, “Evaluating Bayesian Deep Learning Methods for Semantic Segmentation,” in arXiv Preprint, 2018.
- H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid Scene Parsing Network,” in Conference on Computer Vision and Pattern Recognition, 2017.
- T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. Zitnick, “Microsoft COCO: Common Objects in Context,” in European Conference on Computer Vision, 2014, pp. 740–755.
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