Advertisement

Sports Video Analysis: From Semantics to Tactics

  • Guangyu Zhu
  • Changsheng Xu
  • Qingming Huang
Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

Sports content is expected to be a key driver for compelling new infotainment applications and services because of its mass appeal and inherent structures that are amenable for automatic processing. Due to its wide viewership and tremendous commercial value, there has been an explosive growth in the research area of sports video analysis. The existing work on sports video analysis can be classified as two perspectives in terms of semantic analysis and tactic analysis. For semantic analysis, the objective is to detect the semantic events in the sports video and present them to the common users. Most of the current effort of semantic analysis of sports videos is devoted to event detection. Event detection is essential for sports video summarization, indexing, and retrieval, and extensive research efforts have been devoted to this area. However, the previous approaches rely heavily on video content itself and require the whole video content for event detection. Due to the semantic gap between low-level features and high-level events, it is difficult to come up with a generic framework to achieve a high accuracy of event detection. In addition, the dynamic structures from different sports domains further complicate the analysis and impede the implementation of live-event detection systems. In this chapter, we present a novel approach for event detection from the live sports game using Web-casting text and broadcast video. Moreover, we give scenarios to illustrate how to apply the proposed solution to professional and consumer services. In contrast, tactic analysis aims to recognize and discover tactic patterns in the games and present the results to the professionals in the tactic mode. Most of existing approaches on event detection in sports video are general audience oriented, where the extracted events are then presented to the audience without further analysis. However, professionals such as soccer coaches are more interested in the tactics used in the events. Consequently, we present a novel approach to extract tactic information from the goal event in broadcast soccer video and present the goal event in a tactic mode to the coaches and sports professionals.

Keywords

Support Vector Regression Goal Event Sport Video Soccer Game Soccer Video 
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.

Reference

  1. 1.
    Y. Rui, A. Gupta, and A. Acero, “Automatically extracting highlights for TV baseball programs”, In Proc. of ACM Multimedia, Los Angeles, CA, pp. 105–115, 2000.Google Scholar
  2. 2.
    M. Xu, N.C. Maddage, C. Xu, M.S. Kakanhalli, and Q. Tian, “Creating audio keywords for event detection in soccer video”, In Proc. of IEEE International Conference on Multimedia and Expo, Baltimore, USA, Vol. 2, pp. 281–284, 2003.Google Scholar
  3. 3.
    Y. Gong, L.T. Sin, C.H. Chuan, H.J. Zhang, and M. Sakauchi, “Automatic parsing of TV soccer programs”, In Proc. of International Conference on Multimedia Computing and Systems, pp. 167–174, 1995.Google Scholar
  4. 4.
    A. Ekin, A.M. Tekalp, and R. Mehrotra, “Automatic soccer video analysis and summarization”, IEEE Trans. on Image Processing, Vol. 12:7, no. 5, pp. 796–807, 2003.CrossRefGoogle Scholar
  5. 5.
    D. Zhang, and S.F. Chang, “Event detection in baseball video using superimposed caption recognition”, In Proc. of ACM Multimedia, pp. 315–318, 2002.Google Scholar
  6. 6.
    J. Assfalg, M. Bertini, C. Colombo, A. Bimbo, and W. Nunziati, “Semantic annotation of soccer videos: automatic highlights identification,” Computer Vision and Image Understanding (CVIU), Vol. 92, pp. 285–305, November 2003.CrossRefGoogle Scholar
  7. 7.
    R. Radhakrishan, Z. Xiong, A. Divakaran, Y. Ishikawa, "Generation of sports highlights using a combination of supervized & unsupervized learning in audio domain", In Proc. of International Conference on Pacific Rim Conference on Multimedia, Vol. 2, pp. 935–939, December 2003.Google Scholar
  8. 8.
    K. Wan, and C. Xu, “Robust soccer highlight generation with a novel dominant-speech feature extractor”, In Proc. of IEEE International Conference on Multimedia and Expo, Taipei, Taiwan, pp. 591–594, 27–30 June 2004.Google Scholar
  9. 9.
    M. Xu, L. Duan, C. Xu, and Q. Tian, “A fusion scheme of visual and auditory modalities for event detection in sports video”, In Proc. of IEEE International Conference on Acoustics, Speech, & Signal Processing, Hong Kong, China, Vol. 3, pp. 189–192, 2003.Google Scholar
  10. 10.
    K. Wan, C. Xu, “Efficient multimodal features for automatic soccer highlight generation”, In Proc. of International Conference on Pattern Recognition, Cambridge, UK, Vol. 3, pp. 973–976, 23–26 August 2004.Google Scholar
  11. 11.
    M. Xu, L. Duan, C. Xu, M.S. Kankanhalli, and Q. Tian, “Event detection in basketball video using multi-modalities”, In Proc. of IEEE Pacific Rim Conference on Multimedia, Singapore, Vol. 3, pp. 1526–1530, 15–18 December 2003.Google Scholar
  12. 12.
    M. Han, W. Hua, W. Xu, and Y. Gong, “An integrated baseball digest system using maximum entropy method”, In Proc. of ACM Multimedia, pp. 347–350, 2002.Google Scholar
  13. 13.
    S. Nepal, U. Srinivasan, and G. Reynolds, “Automatic detection of goal segments in basketball videos, In Proc. of ACM Multimedia, Ottawa, Canada, pp. 261–269, 2001.Google Scholar
  14. 14.
    J. Wang, C. Xu, E.S. Chng,, K. Wan, and Q. Tian, “Automatic generation of personalized music sports video”, In Proc. of ACM International Conference on Multimedia, Singapore, pp. 735–744, 6–11 November 2005.Google Scholar
  15. 15.
    N. Nitta and N. Babaguchi, “Automatic story segmentation of closed-caption text for semantic content analysis of broadcasted sports video,” In Proc. of 8th International Workshop on Multimedia Information Systems ’02, pp. 110–116, 2002.Google Scholar
  16. 16.
    N. Babaguchi, Y. Kawai, and T. Kitahashi, “Event based indexing of broadcasted sports video by intermodal collaboration,” IEEE Trans. on Multimedia, Vol. 4, pp. 68–75, March 2002.CrossRefGoogle Scholar
  17. 17.
    N. Nitta, N. Babaguchi, and T. Kitahashi, “Generating semantic descriptions of broadcasted sports video based on structure of sports game,” Multimedia Tools and Applications, Vol. 25, pp. 59–83, January 2005.CrossRefGoogle Scholar
  18. 18.
    H. Xu and T. Chua, “The fusion of audio-visual features and external knowledge for event detection in team sports video,” In Proc. of Workshop on Multimedia Information Retrieval (MIR’04), October 2004.Google Scholar
  19. 19.
    H. Xu and T. Chua, “Fusion of multiple asynchronous information sources for event detection in soccer video”, In Proc. of IEEE ICME’05, Amsterdam, Netherlands, pp. 1242–1245, 2005.Google Scholar
  20. 20.
    C. Xu, J. Wang, K. Wan, Y. Li and L. Duan, “Live sports event detection based on broadcast video and web-casting text”, In Proc of ACM Multimedia, pp. 221–230, 2006.Google Scholar
  21. 21.
  22. 22.
  23. 23.
    M. Bertini, R. Cucchiara, A.D. Bimbo, and A. Prati, “Object andevent detection for semantic annotation and transcoding,” in Proc.IEEE Int. Conf. Multimedia and Expo, Baltimore, MD, pp.421–424, July 2003.Google Scholar
  24. 24.
    R. Leonardi and P. Migliorati, “Semantic indexing of multimedia documents,” IEEE Multimedia, Vol. 9, pp. 44–51, April–June 2002.CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Y. Tan and et al., “Rapid estimation of camera motion from compressed video with application to video annotation,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 10–11, pp. 133–146, 2000.Google Scholar
  27. 27.
    Y. Li, C. Xu, K. Wan, X. Yan, and X. Yu, Reliable video clock time recognition, In Proc. of Intl. Conf. Pattern Recognition, Hong Kong, 20–24, August 2006.Google Scholar
  28. 28.
    G. Sudhir, J.C.M. Lee, and A.K. Jain, “Automatic classification of tennis video for high-level content-based retrieval,” In Proc. Int. Workshop on Content-Based Access of Image and Video Databases, pp. 81–90, 1998.Google Scholar
  29. 29.
    J.R. Wang, and N. Parameswaran, “Analyzing tennis tactics from broadcasting tennis video clips,” In Proc. Int. Conf. Multimedia Modeling, pp. 102–106, 2005.Google Scholar
  30. 30.
    P. Wang, R. Cai, and S.Q. Yang, “A tennis video indexing approach through pattern discovery in interactive process,” In Proc. Pacific-Rim Conf. Multimedia, pp. 49–56, 2004.Google Scholar
  31. 31.
    G. Zhu, C. Xu, Q. Huang, W. Gao, and L. Xing, “Player action recognition in broadcast tennis video with applications to semantic analysis of sports game,” In Proc. ACM Multimedia, pp. 431–440, 2006.Google Scholar
  32. 32.
    T. Taki, J. Hasegawa, and T. Fukumura, “Development of motion analysis system for quantitative evaluation of teamwork in soccer games,” In Proc. Int. Conf. Image Processing, Vol. 3, pp. 815–818, 1996.Google Scholar
  33. 33.
    S. Hirano, and S. Tsumoto, “Finding interesting pass patterns from soccer game records,” In Proc. Eur. Conf. Principles and Practice of Knowledge Discovery in Databases, Vol. 3202, pp. 209–218, 2004.Google Scholar
  34. 34.
    G. Zhu, Q. Huang, C. Xu, Y. Rui, S. Jiang, W. Gao and H. Yao, “Trajectory based event tactics analysis in broadcast sports video,” ACM Multimedia, pp. 58–67, 2007.Google Scholar
  35. 35.
    C.H. Kang, J.R. Hwang, and K.J. Li, “Trajectory analysis for soccer players,” In Proc. Int. Conf. Data Mining Workshops, pp. 377–381, 2006.Google Scholar
  36. 36.
    G. Zhu, C. Xu, Q. Huang, and W. Gao, “Automatic multi-player detection and tracking in broadcast sports video using support vector machine and particle filter,” In Proc. Int. Conf. Multimedia & Expo, pp. 1629–1632, 2006.Google Scholar
  37. 37.
    S. Jiang, Q. Ye, W. Gao, T. Huang, “A new method to segment playfield and its applications in match analysis in sports video,” ACM Multimedia, pp. 292–295, 2004.Google Scholar
  38. 38.
    Q. Ye, W. Gao, W. Zeng, “Color image segmentation using density-based clustering,” IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 345–348, 2003.Google Scholar
  39. 39.
    V. Vapnik, The nature of statistical learning theory, Springer-Verlag, New York, 1995.MATHGoogle Scholar
  40. 40.
    P. Perez, C. Hue, J. Vermaak, M. Gangnet, “Color-based probabilistic tracking,” European Conference on Computer Vision, pp. 661–675, 2002.Google Scholar
  41. 41.
    G. Zhu, D. Liang, Y. Liu, Q. Huang, W. Gao, “Improving particle filter with support vector regression for efficient visual tracking,” IEEE International Conference on Image Processing, Vol. 2, pp. 422–425, 2005.Google Scholar
  42. 42.
    D. Liang, Y. Liu, Q. Huang, and W. Gao, “A scheme for ball detection and tracking in broadcast soccer video,” In Proc. Pacific-Rim Conf. Multimedia, pp. 864–875, 2005.Google Scholar
  43. 43.
    F. Dufaux and J. Konrad, “Efficient, robust, and fast global motion estimation for video coding,” IEEE Trans. Image Processing, Vol. 9, no. 3, pp. 497–501, 2000.CrossRefGoogle Scholar
  44. 44.
    J. Wang, C. Xu, E. Chng, K. Wan and Q. Tian, “Automatic replay generation for soccer video broadcasting,” In Proc. ACM Multimedia, pp. 32–39, 2004.Google Scholar
  45. 45.
    G.A. Korn and T.M. Korn, Math. Handbook for Scientists and Engineers, New York: McGraw-Hill, 1968.Google Scholar
  46. 46.
    J. Chin, V. Diehl, and K. Norman, “Development of an instrument measuring user satisfaction of the human-computer interface,” In Proc. SIGCHI on Human Factors in CS, pp. 213–218, 1998.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.NEC Laboratories AmericaUSA

Personalised recommendations