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Robust visual tracking via part-based model

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Abstract

In this paper, we propose a novel visual object tracking method using a part based appearance model. First, a local kernel feature is developed to encode edge information of patches. Next, bounding box of the target is divided into multiple parts. Then, each part uses correlation filter based tracking to predict position in the next frame. The matrix cosine similarity is utilized to measure reliabilities of the patches. Finally, optimal target location is predicted via maximizing the likelihood, which is obtained by adaptively fusing the reliable patches locations. Experimental results illustrate that our algorithm outperforms state-of-the-art tracking methods significantly in terms of accuracy and robustness.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China No. 61873246.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Yong Wang & Lu Ding

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Yong Wang

  3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinbin Luo & Shan Fu

  4. College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Huanlong Zhang

Authors
  1. Yong Wang
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  2. Xinbin Luo
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  3. Lu Ding
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  4. Shan Fu
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  5. Huanlong Zhang
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Corresponding author

Correspondence to Xinbin Luo.

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Communicated by Q. Tian.

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Wang, Y., Luo, X., Ding, L. et al. Robust visual tracking via part-based model. Multimedia Systems 26, 607–620 (2020). https://doi.org/10.1007/s00530-020-00668-3

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