Semantic Event Detection in Sports Through Motion Understanding

  • N. Rea
  • R. Dahyot
  • A. Kokaram
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3115)


In this paper we investigate the retrieval of semantic events that occur in broadcast sports footage. We do so by considering the spatio-temporal behaviour of an object in the footage as being the embodiment of a particular semantic event. Broadcast snooker footage is used as an example of the sports footage for the purpose of this research. The system parses the sports video using the geometry of the content in view and classifies the footage as a particular view type. A colour based particle filter is then employed to robustly track the snooker balls, in the appropriate view, to evoke the semantics of the event. Over the duration of a player shot, the position of the white ball on the snooker table is used to model the high level semantic structure occurring in the footage. Upon collision of the white ball with another coloured ball, a separate track is instantiated allowing for the detection of pots and fouls, providing additional clues to the event in progress.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bertini, M., Bimbo, A.D., Nunziati, W.: Semantic annotation for live and posterity logging of video documents. In: Visual Communications and Image Processing (VCIP 2003) (2003)Google Scholar
  2. 2.
    Kijak, E., Gros, P., Oisel, L.: Temporal structure analysis of broadcast tennis video using hidden markov models. In: SPIE Storage and Retrieval for Media Databases, 289–299 (2003)Google Scholar
  3. 3.
    Assfalg, J., Bertini, M., Bimbo, A.D., Nunziati, W., Pala, P.: Soccer highlight detecti n and recognition using hmms. In: IEEE International Conference on Multimedia and Expo (2002)Google Scholar
  4. 4.
    Djeraba, C.: Content-based multimedia indexing and retrieval. IEEE Multimedia 9, 52–60 (2002)CrossRefGoogle Scholar
  5. 5.
    Chang, P., Han, M., Gong, Y.: Extract highlights from baseball game video with hidden markov models. In: Proceedings of the International Conference on Image Processing, ICIP 2002 (2002)Google Scholar
  6. 6.
    Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic soccer video analysis and summariz ation. In: International Conference on Electronic Imaging: Storage and Retrieval for Media Databases, 339–350 (2003)Google Scholar
  7. 7.
    Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Colour based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Denman, H., Rea, N., Kokaram, A.C.: Content based analysis for video from snooker broadcasts. Journal of Computer Vision and Image Understanding (CVIU): Special Issue on Video Retrieval and Summarization 92, 141–306 (2003)Google Scholar
  9. 9.
    Lee, J.J., Kim, J., Kim, J.H.: Data-driven design of hmm topology for on-line handwriting recognition. In: The 7th International Workshop on Frontiers in Handwriting Recognition (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • N. Rea
    • 1
  • R. Dahyot
    • 1
    • 2
  • A. Kokaram
    • 1
  1. 1.Electronic and Electrical Engineering DepartmentUniversity of Dublin, Trinity CollegeDublinIreland
  2. 2.University of CambridgeCambridgeUnited Kingdom

Personalised recommendations