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International Symposium on Visual Computing

ISVC 2009: Advances in Visual Computing pp 757–766Cite as

Group Action Recognition Using Space-Time Interest Points

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5876))

Abstract

Group action recognition is a challenging task in computer vision due to the large complexity induced by multiple motion patterns. This paper aims at analyzing group actions in video clips containing several activities. We combine the probability summation framework with the space-time (ST) interest points for this task. First, ST interest points are extracted from video clips to form the feature space. Then we use k-means for feature clustering and build a compact representation, which is then used for group action classification. The proposed approach has been applied to classification tasks including four classes: badminton, tennis, basketball, and soccer videos. The experimental results demonstrate the advantages of the proposed approach.

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Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, 100190, P.R. China

    Qingdi Wei, Xiaoqin Zhang & Weiming Hu

  2. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, P.R. China

    Yu Kong

  3. Center for Information Science and Technology, Computer and Information Science Department, Temple University, Philadelphia, PA, USA

    Haibin Ling

Authors
  1. Qingdi Wei
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  2. Xiaoqin Zhang
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  3. Yu Kong
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  4. Weiming Hu
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  5. Haibin Ling
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, 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. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, 338-8570, Japan

    Yoshinori Kuno

  6. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  7. Univ. of Zurich, Department of Informatics, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  8. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  9. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  10.  ,  

    Miguel L. Encarnação

  11. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  12. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Cite this paper

Wei, Q., Zhang, X., Kong, Y., Hu, W., Ling, H. (2009). Group Action Recognition Using Space-Time Interest Points. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_72

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  • DOI: https://doi.org/10.1007/978-3-642-10520-3_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10519-7

  • Online ISBN: 978-3-642-10520-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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