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Spatio-Temporal Correlation Graph for Association Enhancement in Multi-object Tracking

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Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

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Abstract

Due to the frequent interaction between targets in real-world scenarios, various data association problems, such as association ambiguities and association failure, are caused by potential correlation between interactive tracklets, especially during crowded and cluttered scenes. To overcome the non-intuitionistic of tracklet interaction, spatio-temporal correlation graph (STCG) is proposed to model the potential correlation between pairwise tracklets. Three primitive interactions (aggregation, abruption, stability) are defined to model the completed period of the tracklet interaction. Furthermore, STCG model is applied into network flow tracking to exploit the potential correlation between tracklets and enhance the association of the interactive tracklets, especially when overlapping or occlusion is happened. Our method is effective on MOT challenge benchmarks and achieves considerable competitive results with current state-of-the-art trackers.

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Change history

  • 23 February 2020

    Unfortunately the authors of this contribution missed to add an acknowledgment. The acknowledgment should read as follows:

    Acknowledgement:

    This study is partially supported by the National Key R&D Program of China (No.2017YFC0806500), the National Natural Science Foundation of China (No.61861166002), the Science and Technology Development Fund of Macau SAR (File no. 0001/2018/AFJ) Joint Scientific Research Project, the Macao Science and Technology Development Fund (No.138/2016/A3), the Fundamental Research Funds for the Central Universities, the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2019ZX-04) and the China Scholarship Council State-Sponsored Scholarship Program (Grant No. 201806025026). Thank you for the support from HAWKEYE Group.

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Acknowledgement

This study is partially supported by the National Key R&D Program of China (No. 2017YFC0806500), the National Natural Science Foundation of China (No. 61861166002), the Science and Technology Development Fund of Macau SAR (File no. 0001/2018/AFJ) Joint Scientific Research Project, the Macao Science and Technology Development Fund (No.138/2016/A3), the Fundamental Research Funds for the Central Universities, the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2019ZX-04) and the China Scholarship Council State-Sponsored Scholarship Program (Grant No. 201806025026). Thank you for the support from HAWKEYE Group.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, China

    Zhijie Zhong, Hao Sheng, Yang Zhang, Yubin Wu & Jiahui Chen

  2. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China

    Zhijie Zhong, Hao Sheng, Yang Zhang, Yubin Wu & Jiahui Chen

  3. Macao Polytechnic Institute, Macao, China

    Wei Ke

Authors
  1. Zhijie Zhong
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  2. Hao Sheng
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  3. Yang Zhang
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  4. Yubin Wu
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  5. Jiahui Chen
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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 Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Zhong, Z., Sheng, H., Zhang, Y., Wu, Y., Chen, J., Ke, W. (2019). Spatio-Temporal Correlation Graph for Association Enhancement in Multi-object Tracking. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_35

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

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