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A Video Surveillance System Based on Gait Recognition

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Biometric Recognition (CCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10996))

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Abstract

Gait recognition is a biometric technology with unique advantages over other conventional ones, and its wide applications are yet to come. The proposed system applies gait recognition over existing video camera networks, converting them into powerful surveillance systems. It provides an efficient way of searching through the accumulated videos, saving human reviewers from tedious and inefficient work. The system also enables various scenarios from different cameras to be processed in parallel so different equipment at different locations can be coordinated to work together thus greatly improve the efficiency for searching and tracing subject persons. The system is adopted by policing department and has showed outstanding robustness and effectiveness.

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Author information

Authors and Affiliations

  1. Tianjin Academy for Intelligent Recognition Technologies, TEDA MSD-G1 10F, TEDA Tianjin Economic-Technological Development Area, Tianjin, China

    Dexin Zhang

  2. School of Electronic and Information Engineering, Ningbo University of Technology, 201 Fenghua Road, Ningbo, 315211, Zhejiang, China

    Haoxiang Zhang

Authors
  1. Dexin Zhang
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  2. Haoxiang Zhang
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Corresponding author

Correspondence to Haoxiang Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Xinjiang University, Urumqi, China

    Zhenhong Jia

  5. Tsinghua University, Beijing, China

    Jianjiang Feng

  6. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  7. Xinjiang University, Urumqi, China

    Kurban Ubul

  8. Tsinghua University, Shenzhen, China

    Zhenhua Guo

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Cite this paper

Zhang, D., Zhang, H. (2018). A Video Surveillance System Based on Gait Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-97909-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97908-3

  • Online ISBN: 978-3-319-97909-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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