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Robust Part-Based Correlation Tracking

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

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Abstract

Visual tracking is a challenging task where the target may undergo background clutters, deformation, severe occlusion and out-of-view in video sequences. In this paper, we propose a novel tracking method, which utilizes representative parts of the target to handle occlusion situations. For the sake of efficiency, we train a classifier for each part using correlation filter which has been used in visual tracking recently due to its computational efficiency. In addition, we exploit the motion vectors of reliable parts between two consecutive frames to estimate the position of the object target and we utilize the spatial relationship between representative part and target center to estimate the scale of the target. Furthermore, part models are adaptively updated to avoid introducing errors which can cause model drift. Extensive experiments show that our algorithm is comparable to state-of-the-art methods on visual tracking benchmark in terms of accuracy and robustness.

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Acknowledgments

The work is supported by National High-Tech R&D Program (863 Program) under Grant 2015AA016402 and Shanghai Natural Science Foundation under Grant 14Z111050022.

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

    Xiaodong Liu & Yue Zhou

Authors
  1. Xiaodong Liu
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  2. Yue Zhou
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Corresponding author

Correspondence to Yue Zhou .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Liu, X., Zhou, Y. (2016). Robust Part-Based Correlation Tracking. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_71

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  • DOI: https://doi.org/10.1007/978-3-319-46672-9_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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