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Robust visual tracking via a hybrid correlation filter

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Abstract

In this paper, we propose a hybrid correlation filter based tracking method which depends on coupled interactions between a global filter and two local filters. Specifically, a local kernel feature with Gaussian curvature is developed to encode object appearance. Then the global filter and the two local filters independently track the target. The peak-to-sidelobe ratio (PSR) is employed to measure the reliability of the tracking results. Next, the global filter and the two local filters jointly determine the target position. In this way, the proposed hybrid model deals well with challenging situations, e.g., partial occlusion and scale changes. Experiments on large benchmark datasets show that our method performs favorably against state-of-the-art trackers.

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Acknowledgments

This paper is jointly supported by the National Natural Science Foundation of China (61305016) and Fundamental Research Funds for the Central Universities (GrantNo.JUSRP1059). We thank the anonymous editor and reviewers for their careful reading and many insightful comments and suggestions.

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Authors and Affiliations

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

    Yong Wang, Lu Ding & Shan Fu

  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

  4. School of Mechanical Engineering, Jiangnan University, Wuxi, China

    Jingjing Wu

Authors
  1. Yong Wang
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  2. Xinbin Luo
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  3. Lu Ding
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  4. Jingjing Wu
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  5. Shan Fu
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Correspondence to Xinbin Luo.

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Wang, Y., Luo, X., Ding, L. et al. Robust visual tracking via a hybrid correlation filter. Multimed Tools Appl 78, 31633–31648 (2019). https://doi.org/10.1007/s11042-019-07851-3

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  • DOI: https://doi.org/10.1007/s11042-019-07851-3

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