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SVM-HMM Based Human Behavior Recognition

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Human Centered Computing (HCC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8944))

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Abstract

In the field of human behavior recognition, a GMM-HMM system shows lower classification performance in short image sequences. The SVM-HMM, which has been successfully used in speech recognition, is introduced into behavior recognition in this paper. As one of the discriminate models, SVM is able to use less training samples to distinguish the differences of categories than GMM. Therefore, the part of GMM in the GMM-HMM system is replaced by SVM. The experimental results show that the SVM-HMM system achieves better performance for both short and long image sequences.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Shanshan Han, Minfei Zhang, Penglin Li & Jinjie Yao

Authors
  1. Shanshan Han
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  2. Minfei Zhang
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  3. Penglin Li
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  4. Jinjie Yao
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Corresponding author

Correspondence to Shanshan Han .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe, Middlesex, United Kingdom

    Bo Hu

  3. Fudan University, Shanghai, China

    Ning Gu

  4. Telecommunications and Information Communication Technology, National Institute of Posts, Phnom Penh, Cambodia

    Sopheap Seng

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Han, S., Zhang, M., Li, P., Yao, J. (2015). SVM-HMM Based Human Behavior Recognition. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-15554-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15553-1

  • Online ISBN: 978-3-319-15554-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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