Abstract
Detecting and tracking vehicles in urban scenes is a crucial step in many traffic-related applications as it helps to improve road user safety among other benefits. Various challenges remain unresolved in multi-object tracking (MOT) including target information description, long-term occlusions and fast motion. We propose a multi-vehicle detection and tracking system following the tracking-by-detection paradigm that tackles the previously mentioned challenges. Our MOT method extends an Intersection-over-Union (IOU)-based tracker with vehicle re-identification features. This allows us to utilize appearance information to better match objects after long occlusion phases and/or when object location is significantly shifted due to fast motion. We outperform our baseline MOT method on the UA-DETRAC benchmark while maintaining a total processing speed suitable for online use cases.
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References
Bochinski, E.: High-speed tracking-by-detection without using image information. In: International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017 (2017)
Bochinski, E.: Extending IOU Based multi-object tracking by visual information. In: IEEE International Conference on Advanced Video and Signals-Based Surveillance, pp. 441–446 (2018)
Wu, C., Liu, C., Chiang, C., Tu, W., Chien, S.: Vehicle re-identification with the space-time prior. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018)
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, London (2011). https://doi.org/10.1007/978-1-84882-935-0
Fiaz, M.: Handcrafted and deep trackers: recent visual object tracking approaches. ACM Comput. Survey 52, 1–44 (2019)
Li, W.: Multiple object tracking with motion and appearance cues. In: IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 161–169 (2019)
Ren, S.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Patt. Anal. Mach. Intell. 39, 1137–1149 (2017)
He, K.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)
Redmon, J.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Zhou, X.: Objects as Points. ArXiv, abs/1904.07850 (2019)
Perreault, H.: SpotNet: self-attention multi-task network for object detection. In: 2020 17th Conference on Computer and Robot Vision (CRV), pp. 230–237 (2020)
Dicle, C.: The way they move: tracking multiple targets with similar appearance. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2304–2311 (2013)
Rezatofighi, S.: Joint probabilistic data association revisited. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3047–3055 (2015)
Pirsiavash, H.: Globally-optimal greedy algorithms for tracking a variable number of objects. CVPR 2011, 1201–1208 (2011)
Kalman, E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)
Kuhn, H. W.: The Hungarian method for the assignment problem. Naval Res. Logis. Q. 2, 83–97 (1955)
Bewley, A.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468 (2016)
Wojke, N.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649 (2017)
Sun, S.: Deep affinity network for multiple object tracking. IEEE Trans. Patt. Anal. Mach. Intell. 43, 104–119 (2021)
Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., Yang, M.-H.: Online multi-object tracking with dual matching attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 379–396. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_23
Kieritz, H.: Joint detection and online multi-object tracking. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), pp. 1459–1467 (2018)
Milan, A.: Online multi-target tracking using recurrent neural networks. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4225–4232 (2017)
Braso, G.: Learning a neural solver for multiple object tracking. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6246–6256 (2020)
Lee, S.: multiple object tracking via feature pyramid Siamese networks. IEEE Access 7, 8181–8194 (2019)
Osep, A.: Track, then decide: category-agnostic vision-based multi-object tracking. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3494–3501 (2018)
Newell, A.: Stacked Hourglass networks for human pose estimation. ECCV (2016)
St-Charles, P.-L.: A self-adjusting approach to change detection based on background word consensus. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 990–997 (2015)
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50
Wen, L.: UA-DETRAC: a new benchmark and protocol for multi-object detection and tracking. Comput. Vis. Image Underst 193, 102907 (2020)
Miah, M.: An empirical analysis of visual features for multiple object tracking in urban scenes. In: International Conference on Pattern Recognition (ICPR) (2020)
He, K.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Kang, Z.: Multiple Object Tracking in Videos. Master’s thesis, Department of computer engineering, École Polytechnique de Montréal (2021)
Kalal, Z.: Forward-backward error: automatic detection of tracking failures. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, pp. 2756–2759 (2010)
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Messoussi, O. et al. (2021). Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_15
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