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Coupling Multiple Alignments and Re-ranking for Low-Latency Online Multi-target Tracking

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

Previous works for multi-target tracking employ two strategies: global optimization and online state estimation. In time-critical applications, the former methods have long temporal latency, and the latter can’t recover from erroneous association or drifting. In this paper, we combine these two strategies, and propose a new low-latency online tracking approach. Unlike previous multi-hypotheses methods, which are always suffered from combinational explosion, our approach keeps the candidate associations using multiple alignments only in ambiguous cases. The novel features based on previous multi-frame associations are designed for re-ranking of the multiple linkages. The experimental results illustrate the advantage and robustness of these features based on prediction of previously generated tracks, and their discrimination to find optimal ones. Comparison with five state-of-the-art methods proves that our proposed method is competitive to global optimal ones and is superior to other online tracking algorithms.

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Acknowledgement

This work was supported in part by National Basic Research Program of China (973 Program): 2012CB316400, in part by National Natural Science Foundation of China: 61025011, 61133003, 61332016, 61390510.

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China

    Yingkun Xu, Lei Qin & Qingming Huang

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

Authors
  1. Yingkun Xu
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  2. Lei Qin
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  3. Qingming Huang
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Corresponding author

Correspondence to Yingkun Xu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Xu, Y., Qin, L., Huang, Q. (2015). Coupling Multiple Alignments and Re-ranking for Low-Latency Online Multi-target Tracking. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_35

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

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