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Group Guided Data Association for Multiple Object Tracking

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13847))

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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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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Yubin Wu, Hao Sheng, Shuai Wang, Yang Liu & Zhang Xiong

  2. Beihang Hangzhou Innovation Institute Yuhang, Xixi Octagon City, Hangzhou, Yuhang District, China

    Yubin Wu, Hao Sheng, Shuai Wang, Yang Liu & Zhang Xiong

  3. Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China

    Hao Sheng, Zhang Xiong & Wei Ke

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  1. Yubin Wu
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  2. Hao Sheng
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  3. Shuai Wang
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  4. Yang Liu
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  5. Zhang Xiong
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  6. Wei Ke
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Corresponding author

Correspondence to Hao Sheng .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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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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  • DOI: https://doi.org/10.1007/978-3-031-26293-7_29

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