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Primitive Human Action Recognition Based on Partitioned Silhouette Block Matching

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Advances in Visual Computing (ISVC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8034))

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Abstract

This paper deals with the issue of recognizing primitive human actions through template matching with time series silhouette images. Although existing methods based on this simple approach can recognize a subject’s action from a low-resolution image sequence, which is a basic requirement for surveillance applications, their recognition accuracy decreases considerably for corrupted silhouettes due to occlusion. To deal with this problem while keeping algorithm simplicity, we propose a novel method, which integrates template matching results for temporally and spatially partitioned silhouette blocks. Experimental results indicate that our method outperforms the existing methods in the accuracy of action recognition for corrupted silhouettes.

This work was supported in part by the Japan Society for the Promotion of Science (JSPS) under a Grant-in-Aid for Scientific Research (C) (No.23500201).

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References

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Author information

Authors and Affiliations

  1. Tohoku University, Katahira 2-1-1, Aoba-ku, Sendai, 980-8577, Japan

    Toru Abe

  2. Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan

    Masaru Fukushi

  3. SAXA, Inc., Miyashimo 3-14-15, Chuo-ku, Sagamihara, 252-5221, Japan

    Daisuke Ueda

Authors
  1. Toru Abe
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  2. Masaru Fukushi
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  3. Daisuke Ueda
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Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Arizona State University, Tempe, AZ, USA

    Baoxin Li

  6. Mitsubishi Electric Research Laboratories, Cambridge, MA, USA

    Fatih Porikli

  7. University of California, Riverside, CA, USA

    Victor Zordan

  8. AT&T Research Labs, Florham Park, NJ, USA

    James Klosowski

  9. ZIRST, Saint-Ismier Cedex, France

    Sabine Coquillart

  10. Qualcomm Research, San Diego, CA, USA

    Xun Luo

  11. Oxford e-Research Centre, University of Oxford,, Oxford, UK

    Min Chen

  12. IBM, Hawthorne, NY, USA

    David Gotz

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Abe, T., Fukushi, M., Ueda, D. (2013). Primitive Human Action Recognition Based on Partitioned Silhouette Block Matching. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_30

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  • DOI: https://doi.org/10.1007/978-3-642-41939-3_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41938-6

  • Online ISBN: 978-3-642-41939-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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