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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2016: PRICAI 2016: Trends in Artificial Intelligence pp 700–712Cite as

Set to Set Visual Tracking

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9810)

Abstract

Sparse representation has been widely used in visual tracking and achieves superior tracking results. However, most sparse representation models represent the target candidate as a linear combination of target templates and need to solve a sparse optimization problem. In this paper, we propose a novel set to set visual tracking (SSVT) method. Under the particle filter framework, we consider both the target candidates and target templates as image sets, and model them as convex hulls. Then the distance between two image sets is minimized and the tracking result is the target candidate with the maximum coefficient. As the target candidates are modeled as one convex hull, SSVT utilizes the underlying relationship of the target candidates. Moreover, SSVT is very efficient in that it only needs to solve one quadratic optimization problem rather than sparse optimization problems. Both qualitative and quantitative analyses on several challenging image sequences show that the proposed SSVT algorithm outperforms the state-of-the-art trackers.

Keywords

  • Set to set distance
  • Visual tracking
  • Particle filter
  • Convex hull
  • Support vector machine

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  • DOI: 10.1007/978-3-319-42911-3_59
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Acknowledgement

This work was supported by the National Program on Key Basic Research Project under Grant 2013CB329304, the National Natural Science Foundation of China under Grants 61502332, 61432011, 61222210.

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Wencheng Zhu, Pengfei Zhu, Qinghua Hu & Changqing Zhang

Authors
  1. Wencheng Zhu
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  2. Pengfei Zhu
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  3. Qinghua Hu
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  4. Changqing Zhang
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Corresponding author

Correspondence to Pengfei Zhu .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

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© 2016 Springer International Publishing Switzerland

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Zhu, W., Zhu, P., Hu, Q., Zhang, C. (2016). Set to Set Visual Tracking. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_59

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

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