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Human behavior classification by analyzing periodic motions

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Abstract

Recognizing human action is a critical step in many computer vision applications. In this paper, the problem of human behavior classification is addressed from a periodic motion analysis viewpoint. Our approach uses human silhouettes as motion features that can be obtained efficiently, and then projected it into a lower dimensional space where matching is performed. After a periodic analysis, each action unit is represented as a closed loop in this lower dimensional space, and matching is done by computing the distances among these loops. The main contributions are twofold: (1) an efficient periodic action feature constructing method is introduced; and (2) the difference between action units with different phase is computed adaptively with a novel distance proposed in this work. To demonstrate the effectiveness of this approach, human behavior classification experiments were performed on an open dataset. Classification results are highly accurate and show that this approach is promising and efficient.

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Authors and Affiliations

  1. School of Physical and Electronic Information, Huaibei Coal Industry Teachers College, Huaibei, 235000, China

    Jiangtao Wang & Debao Chen

  2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Jingyu Yang

Authors
  1. Jiangtao Wang
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  2. Debao Chen
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  3. Jingyu Yang
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Correspondence to Jiangtao Wang.

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Wang, J., Chen, D. & Yang, J. Human behavior classification by analyzing periodic motions. Front. Comput. Sci. China 4, 580–588 (2010). https://doi.org/10.1007/s11704-009-0070-y

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  • DOI: https://doi.org/10.1007/s11704-009-0070-y

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