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Asian Conference on Computer Vision

ACCV 2014: Computer Vision - ACCV 2014 Workshops pp 58–70Cite as

2-D Structure-Based Gait Recognition in Video Using Incremental GMM-HMM

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

Abstract

Gait analysis is a feasible approach for human identification in intelligent video surveillance. However, the effectiveness of the dominant silhouette-based approaches are severely affected by dressing, bag, hair style and the like. In this paper, we propose a useful 2-D structural feature, named skeleton-based feature, effective improvements for human pose estimation in human walking environment and a recognition framework based on GMM-HMM using incremental learning, which can greatly improve the availability of gait traits in intelligent video surveillance. Our skeleton-based feature uses a 15-DOFs, which is effective in eliminating the interference of dressing, bag, hair style and the like, to represent the torso. In addition, to imitate the natural way of human walking, a Hidden Markov Model (HMM) representing the gait dynamics of human walking incrementally evolves from an average human walking model that represents the average motion process of human walking. Our work makes the gait recognition more robust to noise. Experiments on widely adopted databases prove that our proposed method achieves excellent performance.

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Author information

Authors and Affiliations

  1. Laboratory of Intelligence Recognition and Image Processing, Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Rui Pu & Yunhong Wang

Authors
  1. Rui Pu
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  2. Yunhong Wang
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Corresponding author

Correspondence to Rui Pu .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Pu, R., Wang, Y. (2015). 2-D Structure-Based Gait Recognition in Video Using Incremental GMM-HMM. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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