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Probabilistic Integration of Tracking and Recognition of Soccer Players

  • Toshie Misu
  • Atsushi Matsui
  • Simon Clippingdale
  • Mahito Fujii
  • Nobuyuki Yagi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)

Abstract

This paper proposes a method for integrating player trajectories tracked in wide-angle images and identities by face and back-number recognition from images by a motion-controlled camera. In order to recover from tracking failures efficiently, the motion-controlled camera scans and follows players who are judged likely to undergo heavy occlusions several seconds in the future. The candidates of identities for each tracked trajectory are probabilistically modeled and updated at every identification. The degradation due to the passage of time and occlusions are also modeled. Experiments showed the system’s feasibility for automatic real-time formation estimation which will be applied to metadata production with semantic and dynamic information on sports scenes.

Keywords

soccer formation probabilistic integration tracking face recognition back-number recognition 

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References

  1. 1.
    Beetz, M., Kirchlechner, B., Lames, M.: Computerized Real-Time Analysis of Football Games. IEEE Pervasive Computing 4(3), 33–39 (2005)CrossRefGoogle Scholar
  2. 2.
    Nitanda, N., Haseyama, M., Kitajima, H.: Audio Signal Segmentation and Classification Using Fuzzy Clustering. IEICE Trans. D-II J88-D-II(2), 302–312 (2005)Google Scholar
  3. 3.
    Sano, M., Yamada, I., Sumiyoshi, H., Yagi, N.: Automatic Real-Time Selection and Annotation of Highlight Scenes in Televised Soccer. IEICE Trans. Information and Systems E90-D(1), 224–232 (2007)CrossRefGoogle Scholar
  4. 4.
    Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic Soccer Video Analysis and Summarization. IEEE Trans. Image Process. 12(7), 796–807 (2003)CrossRefGoogle Scholar
  5. 5.
    Matsumoto, K., Sudo, S., Saito, H., Ozawa, S.: Optimized Camera Viewpoint Determination System for Soccer Game Broadcasting. In: Proc. MVA 2000, pp. 115–118 (2000)Google Scholar
  6. 6.
    Figueroa, P.J., Leite, N.J., Barros, R.M.L.: Tracking Soccer Players Aiming their Kinematical Motion Analysis. Computer Vision and Image Understanding 101(2), 122–135 (2006)CrossRefGoogle Scholar
  7. 7.
    Snoek, C.G.M., Worring, M.: A Review on Multimodal Video Indexing. In: Proc. ICME 2002, vol. 2, pp. 21–24 (2002)Google Scholar
  8. 8.
    Misu, T., Takahashi, M., Tadenuma, M., Yagi, N.: Real-Time Event Detection Based on Formation Analysis of Soccer Scenes. Information Technology Letters (FIT 2005) 4 LI-003, 141–144 (2005) (in Japanese)Google Scholar
  9. 9.
    Chia, A.Y.S., Huang, W., Li, L.: Multiple Objects Tracking with Multiple Hypotheses Graph Representation. In: Proc. ICPR 2006, vol. 1, pp. 638–641 (2006)Google Scholar
  10. 10.
    Matsui, A., Clippingdale, S., Matsumoto, T.: Bayesian Sequential Face Detection with Automatic Re-initialization. In: Proc. ICPR 2008 (to appear, 2008)Google Scholar
  11. 11.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Proc. CVPR 2001, vol. 1, pp. 511–518 (2001)Google Scholar
  12. 12.
    Clippingdale, S., Ito, T.: A Unified Approach to Video Face Detection, Tracking and Reognition. In: Proc. ICIP 1999, p. 232 (1999)Google Scholar
  13. 13.
    Otsu, N.: A Threshold Selection Method from Gray-level Histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Toshie Misu
    • 1
  • Atsushi Matsui
    • 1
  • Simon Clippingdale
    • 1
  • Mahito Fujii
    • 1
  • Nobuyuki Yagi
    • 1
  1. 1.Science & Technical Research LaboratoriesNHK (Japan Broadcasting Corporation)TokyoJapan

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