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Group Guided Data Association for Multiple Object Tracking

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Computer Vision – ACCV 2022 (ACCV 2022)

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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Notes

  1. 1.

    https://motchallenge.net/

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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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Correspondence to Hao Sheng .

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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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