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Real-Time Tracking of Multiple Objects by Linear Motion and Repulsive Motion

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

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Abstract

Successful multi-object tracking requires consistently maintaining object identities and real-time performance. This task becomes more challenging when objects are indistinguishable from one another. This paper presents a Bayesian framework for maintaining the identities of multiple objects. Our semi-independent joint motion model (SIMM) solves the coalescence and identity switching problem in real time. This joint motion model is a non-parametric mixture model that simultaneously captures linear motion and repulsive motion. Linear motion is a constant velocity model, while repulsive motion is described by a repulsive potential in MRF. By maintaining multimodality from multiple motion models, we can infer the appropriate motion model using image evidence and consequently avoid many identity switching errors. Moreover, we develop a new sampling method that does not suffer from the curse of dimensionality because of the availability of high-quality samples. Experimental results show that our approach can track numerous objects in real time and maintain identities under difficult situations.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61001195) and Technology Research and Development Program of Sichuan Province of China (2010JY0078). We would like to thank anonymous reviewers of this paper for helpful comments.

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Authors and Affiliations

  1. Chengdu Sport University, Chengdu, China

    Lejun Shen & Qing Liu

  2. Sichuan University, Chengdu, China

    Zhisheng You

Authors
  1. Lejun Shen
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  2. Zhisheng You
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  3. Qing Liu
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Corresponding author

Correspondence to Lejun Shen .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, 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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Shen, L., You, Z., Liu, Q. (2015). Real-Time Tracking of Multiple Objects by Linear Motion and Repulsive Motion. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

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