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International Conference on Neural Information Processing

ICONIP 2015: Neural Information Processing pp 600–609Cite as

Weighted Joint Sparse Representation Based Visual Tracking

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

Abstract

Aiming at various tracking environments, a weighted joint sparse representation based tracker is proposed. Specifically, each object template is weighted according to its similarity to each candidate. Then all candidates are represented sparsely and jointly, and the sparse coefficients are used to compute the observation probabilities of candidates. The candidate with the maximum observation probability is determined as the object. The object function is solved by a modified accelerated proximal gradient (APG) algorithm. Experiments on several representative image sequences show that the proposed tracking method performs better than the other trackers in the scenarios of illumination variation, occlusion, pose change and rotation.

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Acknowledgment

This work is supported by Scientific Research Fund of Heilongjiang Provincial Education Department (NO: 12541238), Dr. Scientific Research Foundation of Harbin Normal University (KGB201216), the National Science Foundation of China (61173087), Heilongjiang Provincial University Engineering R&D Center of Machine Vision and Intelligent Detection, and Heilongjiang Provincial Education Department Key Laboratory of Intelligent Education and Information Engineering.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Xiping Duan, Jiafeng Liu & Xianglong Tang

  2. College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China

    Xiping Duan

Authors
  1. Xiping Duan
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  2. Jiafeng Liu
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  3. Xianglong Tang
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Corresponding author

Correspondence to Xiping Duan .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Duan, X., Liu, J., Tang, X. (2015). Weighted Joint Sparse Representation Based Visual Tracking. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_68

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  • DOI: https://doi.org/10.1007/978-3-319-26555-1_68

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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