Abstract
Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-Identification (re-ID) in a single network is appealing since it achieves real-time but effective inference on detection and tracking. However, in crowd scenes, the existing MOT methods usually fail to locate occluded objects, which also results in bad effects on the re-ID task. To solve people tracking in crowd scenes, we present a model called HBR (Head-Body-ReID Joint Tracking) to jointly formulates head detection, body detection and re-ID tasks into an uniform framework. Human heads are hardly affected by occlusions in crowd scenes, and they can provide informative clues for whole body detection. The experimental results on MOT17 and MOT20 show that our proposed model performs better than the state-of-the-arts.
Supported by Peng Cheng Laboratory.
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References
Basar, T.: A new approach to linear filtering and prediction problems, pp. 167–179 (2001)
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468 (2016)
Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017)
Cao, J., Weng, X., Khirodkar, R., Pang, J., Kitani, K.: Observation-centric SORT: rethinking SORT for robust multi-object tracking. arXiv preprint arXiv:2203.14360 (2022)
Fang, K., Xiang, Y., Li, X., Savarese, S.: Recurrent autoregressive networks for online multi-object tracking. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 466–475 (2018)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955)
Liang, C., Zhang, Z., Zhou, X., Li, B., Zhu, S., Hu, W.: Rethinking the competition between detection and ReID in multiobject tracking. IEEE Trans. Image Process. 31, 3182–3196 (2022)
Mahmoudi, N., Ahadi, S.M., Rahmati, M.: Multi-target tracking using CNN-based features: CNNMTT. Multimed. Tools Appl. 78(6), 7077–7096 (2018). https://doi.org/10.1007/s11042-018-6467-6
Pang, B., Li, Y., Zhang, Y., Li, M., Lu, C.: TubeTK: adopting tubes to track multi-object in a one-step training model. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6307–6317 (2020)
Peng, J., et al.: Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 145–161. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_9
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 1137–1149 (2017)
Sun, P., et al.: TransTrack: multiple object tracking with transformer. arXiv preprint arXiv:2012.15460 (2020)
Sun, Z., Peng, D., Cai, Z., Chen, Z., Jin, L.: Scale mapping and dynamic re-detecting in dense head detection. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1902–1906 (2018)
Sundararaman, R., De Almeida Braga, C., Marchand, E., Pettre, J.: Tracking pedestrian heads in dense crowd. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3865–3875 (2021)
Voigtlaender, P., et al.: MOTS: multi-object tracking and segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7934–7943 (2019)
Wang, Y., Kitani, K., Weng, X.: Joint object detection and multi-object tracking with graph neural networks. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13708–13715. IEEE (2021)
Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 107–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_7
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649 (2017)
Wu, J., Cao, J., Song, L., Wang, Y., Yang, M., Yuan, J.: Track to detect and segment: an online multi-object tracker. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12347–12356 (2021)
Yang, F., Choi, W., Lin, Y.: Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2129–2137 (2016)
Yu, E., Li, Z., Han, S., Wang, H.: RelationTrack: relation-aware multiple object tracking with decoupled representation. CoRR (2021)
Yu, F., Li, W., Li, Q., Liu, Yu., Shi, X., Yan, J.: POI: multiple object tracking with high performance detection and appearance feature. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 36–42. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_3
Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vis. 129(11), 3069–3087 (2021). https://doi.org/10.1007/s11263-021-01513-4
Zhou, X., Koltun, V., Krähenbühl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 474–490. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_28
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
Zhou, Z., Xing, J., Zhang, M., Hu, W.: Online multi-target tracking with tensor-based high-order graph matching. In: 2018 24th International Conference on Pattern Recognition (ICPR) (2018)
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Liu, Z. et al. (2022). Multiple Object Tracking by Joint Head, Body Detection and Re-Identification. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_16
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