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Sparsity-Based Occlusion Handling Method for Person Re-identification

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MultiMedia Modeling (MMM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8936))

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Abstract

Person re-identification has recently attracted a lot of research interests, it refers to recognizing people across non-overlapping surveillance cameras. However, person re-identification is essentially a very challenging task due to variations in illumination, viewpoints and occlusions. Existing methods address these difficulties through designing robust feature representation or learning proper distance metric. Although these methods have achieved satisfactory performance in the case of illumination and viewpoint changes, seldom of they can genuinely handle the occlusion problem that frequently happens in the real scene. This paper proposes a sparsity-based patch matching method to handle the occlusion problem in the person re-identification. Its core idea is using a sparse representation model to determine the occlusion state of each image patch, which is further utilized to adjust the weight of patch pairs in the feature matching process. Extensive comparative experiments conducted on two widely used datasets have shown the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan, 430072, China

    Bingyue Huang, Jun Chen, Yimin Wang, Chao Liang & Zheng Wang

  2. Research Institute of Wuhan University in Shenzhen, China

    Jun Chen & Chao Liang

  3. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, China

    Kaimin Sun

Authors
  1. Bingyue Huang
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  2. Jun Chen
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  3. Yimin Wang
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  4. Chao Liang
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  5. Zheng Wang
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  6. Kaimin Sun
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Editor information

Editors and Affiliations

  1. University of Technology, P.O. Box 123, 2007, Sydney, NSW, Australia

    Xiangjian He

  2. University of Newcastle, University Dr, Callaghan, 2308, NSW, Australia

    Suhuai Luo

  3. University of Technology, P.O. Box 123, 2007, Sydney, NSW, Australia

    Dacheng Tao  & Muhammad Abul Hasan  & 

  4. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95, Zhongguancun East Road, 100190, Beijing, P.R. China

    Changsheng Xu

  5. Shanghai Jitotong University, 800 Dong Chuan Rd, 200240, Shanghai, China

    Jie Yang

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© 2015 Springer International Publishing Switzerland

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Huang, B., Chen, J., Wang, Y., Liang, C., Wang, Z., Sun, K. (2015). Sparsity-Based Occlusion Handling Method for Person Re-identification. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-14442-9_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14441-2

  • Online ISBN: 978-3-319-14442-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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