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Multi-branch Siamese Networks with Online Selection for Object Tracking

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Advances in Visual Computing (ISVC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

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Abstract

In this paper, we propose a robust object tracking algorithm based on a branch selection mechanism to choose the most efficient object representations from multi-branch siamese networks. While most deep learning trackers use a single CNN for target representation, the proposed Multi-Branch Siamese Tracker (MBST) employs multiple branches of CNNs pre-trained for different tasks, and used for various target representations in our tracking method. With our branch selection mechanism, the appropriate CNN branch is selected depending on the target characteristics in an online manner. By using the most adequate target representation with respect to the tracked object, our method achieves real-time tracking, while obtaining improved performance compared to standard Siamese network trackers on object tracking benchmarks.

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Author information

Authors and Affiliations

  1. LITIV Lab, Polytechnique Montreal, Montreal, Canada

    Zhenxi Li & Guillaume-Alexandre Bilodeau

  2. TELUQ University, Montreal, Canada

    Wassim Bouachir

Authors
  1. Zhenxi Li
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  2. Guillaume-Alexandre Bilodeau
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  3. Wassim Bouachir
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Corresponding author

Correspondence to Zhenxi Li .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Li, Z., Bilodeau, GA., Bouachir, W. (2018). Multi-branch Siamese Networks with Online Selection for Object Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_28

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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