Abstract
Multiple Object Tracking (MOT) usually adopts the Tracking-by-Detection paradigm, which transforms the problem into data association. However, these methods are restricted by detector performance, especially in dense scenes. In this paper, we propose a novel group-guided data association, which improves the robustness of MOT to error detections and increases tracking accuracy in occlusion areas. The tracklets are firstly clustered into groups of related motion patterns by a graph neural network. Using the idea of grouping, the data association is divided into two stages: intra-group and inter-group. For the intra-group, based on the structural relationship between objects, detections are recovered and associated by min-cost network flow. For inter-group, the tracklets are associated with the proposed hypotheses to solve long-term occlusion and reduce false positives. The experiments on the MOTChallenge benchmark prove our method’s effects, which achieves competitive results over state-of-the-art methods.
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Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 941–951 (2019)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008, 1–10 (2008)
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing, pp. 3464–3468. IEEE (2016)
Brasó, G., Leal-Taixé, L.: Learning a neural solver for multiple object tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6247–6257 (2020)
Chen, J., Sheng, H., Li, C., Xiong, Z.: PSTG-based multi-label optimization for multi-target tracking. Comput. Vis. Image Underst. 144, 217–227 (2016)
Chen, X., Qin, Z., An, L., Bhanu, B.: An online learned elementary grouping model for multi-target tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1242–1249 (2014)
Chu, P., Fan, H., Tan, C.C., Ling, H.: Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In: IEEE Winter Conference on Applications of Computer Vision, pp. 161–170. IEEE (2019)
Chu, P., Ling, H.: FAMNet: joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Dai, P., Weng, R., Choi, W., Zhang, C., He, Z., Ding, W.: Learning a proposal classifier for multiple object tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2443–2452 (2021)
Dendorfer, P., et al.: MOT20: a benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578 (2019)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)
Ho, K., Kardoost, A., Pfreundt, F.J., Keuper, J., Keuper, M.: A two-stage minimum cost multicut approach to self-supervised multiple person tracking. In: Proceedings of the Asian Conference on Computer Vision (2020)
Hornakova, A., Henschel, R., Rosenhahn, B., Swoboda, P.: Lifted disjoint paths with application in multiple object tracking. In: International Conference on Machine Learning, pp. 4364–4375. PMLR (2020)
Hornakova, A., Kaiser, T., Swoboda, P., Rolinek, M., Rosenhahn, B., Henschel, R.: Making higher order mot scalable: an efficient approximate solver for lifted disjoint paths. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6330–6340 (2021)
Kratz, L., Nishino, K.: Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 987–1002 (2011)
Leal-Taixé, L., Milan, A., Reid, I., Roth, S., Schindler, K.: MOTchallenge 2015: towards a benchmark for multi-target tracking. arXiv preprint arXiv:1504.01942 (2015)
Liu, Q., Chu, Q., Liu, B., Yu, N.: GSM: graph similarity model for multi-object tracking. In: IJCAI, pp. 530–536 (2020)
Luiten, J., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vision 129(2), 548–578 (2021)
Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)
Milan, A., Schindler, K., Roth, S.: Multi-target tracking by discrete-continuous energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2054–2068 (2015)
Mykheievskyi, D., Borysenko, D., Porokhonskyy, V.: Learning local feature descriptors for multiple object tracking. In: Proceedings of the Asian Conference on Computer Vision (2020)
Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 452–465. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_33
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)
Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2
Sadeghian, A., Alahi, A., Savarese, S.: Tracking the untrackable: learning to track multiple cues with long-term dependencies. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 300–311 (2017)
Sheng, H., Chen, J., Zhang, Y., Ke, W., Xiong, Z., Yu, J.: Iterative multiple hypothesis tracking with tracklet-level association. IEEE Trans. Circuits Syst. Video Technol. 29(12), 3660–3672 (2018)
Sheng, H., et al.: Combining pose invariant and discriminative features for vehicle reidentification. IEEE Internet Things J. 8(5), 3189–3200 (2020)
Sheng, H., et al.: Near-online tracking with co-occurrence constraints in blockchain-based edge computing. IEEE Internet Things J. 8(4), 2193–2207 (2020)
Sheng, H., et al.: High confident evaluation for smart city services. Front. Environ. Sci. 10, 1103 (2022)
Stadler, D., Beyerer, J.: Improving multiple pedestrian tracking by track management and occlusion handling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10958–10967 (2021)
Stadler, D., Beyerer, J.: Multi-pedestrian tracking with clusters. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1–10. IEEE (2021)
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_30
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, S., Sheng, H., Yang, D., Zhang, Y., Wu, Y., Wang, S.: Extendable multiple nodes recurrent tracking framework with RTU++. IEEE Trans. Image Process. 31, 5257–5271 (2022)
Wang, S., Sheng, H., Zhang, Y., Wu, Y., Xiong, Z.: A general recurrent tracking framework without real data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13219–13228 (2021)
Xiang, J., Xu, G., Ma, C., Hou, J.: End-to-end learning deep CRF models for multi-object tracking deep CRF models. IEEE Trans. Circuits Syst. Video Technol. 31(1), 275–288 (2020)
Xu, Y., Chen, Y., Zhang, Y., Zhu, Q., He, Y., Sheng, H.: Bilateral association tracking with Parzen window density estimation. IET Image Processing (2022)
Yang, J., Ge, H., Yang, J., Tong, Y., Su, S.: Online multi-object tracking using multi-function integration and tracking simulation training. Applied Intelligence, pp. 1–21 (2021)
Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Zhang, Y., et al.: Long-term tracking with deep tracklet association. IEEE Trans. Image Process. 29, 6694–6706 (2020)
Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. arXiv preprint arXiv:2110.06864 (2021)
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. Vision 129(11), 3069–3087 (2021)
Zhao, X., Gong, D., Medioni, G.: Tracking using motion patterns for very crowded scenes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 315–328. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33709-3_23
Acknowledgements
This study is partially supported by the National Key R &D Program of China (No.2019YFB2102200), the National Natural Science Foundation of China (No.61872025), the Science and Technology Development Fund, Macau SAR(File no.0001/2018/AFJ), and the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE2021ZX-03). Thank you for the support from the HAWKEYE Group.
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Wu, Y., Sheng, H., Wang, S., Liu, Y., Xiong, Z., Ke, W. (2023). Group Guided Data Association for Multiple Object Tracking. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13847. Springer, Cham. https://doi.org/10.1007/978-3-031-26293-7_29
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