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Exploiting a depth context model in visual tracking with correlation filter

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Abstract

Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Zhao-yun Chen, Lei Luo, Da-fei Huang, Mei Wen & Chun-yuan Zhang

  2. National Key Laboratory of Parallel and Distributed Processing, Changsha, 410073, China

    Zhao-yun Chen, Da-fei Huang, Mei Wen & Chun-yuan Zhang

Authors
  1. Zhao-yun Chen
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  2. Lei Luo
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  3. Da-fei Huang
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  4. Mei Wen
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  5. Chun-yuan Zhang
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Corresponding author

Correspondence to Zhao-yun Chen.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61502509, 61402504, and 61272145), the National High-Tech R&D Program (863) of China (No. 2012AA012706), and the Research Fund for the Doctoral Program of Higher Education of China (No. 21024307130004)

ORCID: Lei LUO, http://orcid.org/0000-0002-9329-1411

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Chen, Zy., Luo, L., Huang, Df. et al. Exploiting a depth context model in visual tracking with correlation filter. Frontiers Inf Technol Electronic Eng 18, 667–679 (2017). https://doi.org/10.1631/FITEE.1500389

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