Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rao, C., Shah, M.: View-invariance in action recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 316–322 (2001)Google Scholar
  2. 2.
    Yacoob, Y., Black, M.J.: Parameterized modeling and recognition of activities. In: IEEE International Conference on Computer Vision, pp. 120–127 (1998)Google Scholar
  3. 3.
    Miyamori, H., Iisaku, S.: Video annotation for content-based retrieval using human behavior analysis and domain knowledge. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 320–325 (2000)Google Scholar
  4. 4.
    Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis, and applications. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(8), 781–796 (2000)CrossRefGoogle Scholar
  5. 5.
    Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: IEEE International Conference on Computer Vision, pp. 726–733 (2003)Google Scholar
  6. 6.
    Zhu, G., Liang, D., Liu, Y., Huang, Q., Gao, W.: Improving particle filter with support vector regression for efficient visual tracking. In: IEEE International Conference on Image Processing, vol. 2, pp. 422–425 (2005)Google Scholar
  7. 7.
    Sudhir, G., Lee, J.C.M., Jain, A.K.: Automatic classification of tennis video for high-level content-based retrieval. In: IEEE International Workshop on Content-Based Access of Image and Video Databases, pp. 81–90 (1998)Google Scholar
  8. 8.
    Pingali, G.S., Jean, Y., Carlbom, I.: Real time tracking for enhanced tennis broadcasts. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 260–265 (1998)Google Scholar
  9. 9.
    Jiang, S., Ye, Q., Gao, W., Huang, T.: A new method to segment playfield and its applications in match analysis in sports video. In: ACM Multimedia, pp. 292–295 (2004)Google Scholar
  10. 10.
    Ye, Q., Gao, W., Zeng, W.: Color image segmentation using density-based clustering. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 345–348 (2003)Google Scholar
  11. 11.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)CrossRefGoogle Scholar
  12. 12.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)CrossRefGoogle Scholar
  13. 13.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefMATHGoogle Scholar
  14. 14.
    Xu, M., Duan, L.Y., Xu, C.S., Tian, Q.: A fusion scheme of visual and auditory modalities for event detection in sports video. In: IEEE International Conference on Acoustics, Seech, and Signal Processing, vol. 3, pp. 189–192 (2003)Google Scholar

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

Personalised recommendations