Action Recognition in Broadcast Tennis Video Using Optical Flow and Support Vector Machine

  • Guangyu Zhu
  • Changsheng Xu
  • Wen Gao
  • Qingming Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3979)


Motion analysis in broadcast sports video is a challenging problem especially for player action recognition due to the low resolution of players in the frames. In this paper, we present a novel approach to recognize the basic player actions in broadcast tennis video where the player is about 30 pixels tall. Two research challenges, motion representation and action recognition, are addressed. A new motion descriptor, which is a group of histograms based on optical flow, is proposed for motion representation. The optical flow here is treated as spatial pattern of noisy measurement instead of precise pixel displacement. To recognize the action performed by the player, support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results demonstrate that our method is promising by integrating with the framework of multimodal analysis in sports video.


Support Vector Machine Optical Flow Action Recognition Broadcast Video Tennis Player 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guangyu Zhu
    • 1
  • Changsheng Xu
    • 2
  • Wen Gao
    • 1
    • 3
  • Qingming Huang
    • 3
  1. 1.Harbin Institute of TechnologyHarbinP.R. China
  2. 2.Institute for Infocomm ResearchSingapore
  3. 3.Graduate School of Chinese Academy of SciencesBeijingP.R. China

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