Machine Learning-Based Soccer Video Summarization System

  • Hossam M. Zawbaa
  • Nashwa El-Bendary
  • Aboul Ella Hassanien
  • Tai-hoon Kim
Part of the Communications in Computer and Information Science book series (CCIS, volume 263)


This paper presents a machine learning (ML) based event detection and summarization system for soccer matches. The proposed system is composed of six phases. Firstly, in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing phase, it applies two types of classification to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the system applies two machine learning algorithms, namely; support vector machine (SVM) and neural network (NN), for emphasizing important segments with logo appearance. Also, in the score board detection phase, the system uses both ML algorithms for detecting the caption region providing information about the score of the game. Subsequently, in the excitement event detection phase, the system uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor filter for detecting goal net. Finally, in the logo-based event detection and summarization phase, the system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. Compared to the performance results obtained using SVM classifier, the proposed system attained good NN-based performance results concerning recall ratio, however it attained poor NN-based performance results concerning precision ratio.


Support Vector Machine Detection Phase Video Shot Video Summarization Sport 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, C.-Y., Wang, J.-C., Wang, J.-F., Hu, Y.-H.: Event-Based Segmentation of Sports Video Using Motion Entropy. In: Ninth IEEE International Symposium on Multimedia (ISM 2007), pp. 107–111 (2007)Google Scholar
  2. 2.
    Chen, C.-Y., Wang, J.-C., Wang, J.-F., Hu, Y.-H.: Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video. EURASIP Journal on Advances in Signal Processing 2008, Article ID 460913 (2008)Google Scholar
  3. 3.
    D’Orazio, T., Leo, M.: A review of vision-based systems for soccer video analysis. Pattern Recognition 43(8), 2911–2926 (2010)CrossRefGoogle Scholar
  4. 4.
    Lotfi, E., Pourreza, H.R.: Event Detection and Automatic Summarization in Soccer Video. In: 4th Iranian Conference on Machine Vision and Image Processing (MVIP 2007), Mashhad, Iran (2007)Google Scholar
  5. 5.
    Yu, B., Zhu, D.H.: Automatic thesaurus construction for spam filtering using revised: back propagation neural network. Journal Expert Systems with Applications 37(1), 24–30 (2010)CrossRefGoogle Scholar
  6. 6.
    Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press (1995)Google Scholar
  7. 7.
    Wu, Q., Zhou, D.-X.: Analysis of support vector machine classification. J. Comput. Anal. Appl. 8, 99–119 (2006)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Ekin, A.: Sports Video Processing for Description, Summarization and Search. PhD Thesis, University of Rochester, Rochester (2003)Google Scholar
  9. 9.
    Xing-hua, S., Jing-yu, Y.: Inference and retrieval of soccer event. Journal of Communication and Computer 4(3) (2007)Google Scholar
  10. 10.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Yeo, S.S.: Logo Detection in Broadcast Soccer Videos Using Support Vector Machine. Submitted to: The 2011 Online Conference On Soft Computing in Industerial Applications WWW (WSC16) (2011)Google Scholar
  11. 11.
    Tjondronegoro, D., Chen, Y.P., Pham, B.: The power of play-break for automatic detection and browsing of self-consumable sport video highlights. In: Multimedia Information Retrieval, pp. 267–274 (2004)Google Scholar
  12. 12.
    Ren, R., Jose, J.M.: Football Video Segmentation Based on Video Production Strategy. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 433–446. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Huang, C.-L., Shih, H.-C., Chao, C.-Y.: Semantic analysis of soccer video using dynamic Bayesian network. IEEE Transactions on Multimedia 8(4) (2006)Google Scholar
  14. 14.
    Zhao, Z., Jiang, S., Huang, Q., Ye, Q.: Highlight summarization in soccer video based on goalmouth detection. In: Asia-Pacific Workshop on Visual Information Processing (2006)Google Scholar
  15. 15.
    Wan, K., Yan, X., Yu, X., Xu, C.: Real-time Goal-Mouth Detection in MPEG Soccer Video. In: Proceedings of ACM MM 2003, Berkeley, USA, pp. 311–314 (2003)Google Scholar
  16. 16.
    Sun, X.: Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In: The IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP 2002), Orlando, Florida, USA, vol. 1, pp. 333–336 (2002)Google Scholar
  17. 17.
    Tjondronegoro, D., Chen, Y.P., Pham, B.: Sports video summarization using highlights and play-breaks. In: The fifth ACM SIGMM International Workshop on Multimedia Information Retrieval (ACM MIR 2003), Berkeley, USA, pp. 201–208 (2003)Google Scholar
  18. 18.
    Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic Soccer Video Analysis and Summarization. IEEE Transactions on Image processing 12(7) (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hossam M. Zawbaa
    • 1
    • 2
  • Nashwa El-Bendary
    • 3
    • 2
  • Aboul Ella Hassanien
    • 1
    • 2
  • Tai-hoon Kim
    • 4
  1. 1.Faculty of Computers and Information, Blind Center of TechnologyCairo UniversityCairoEgypt
  2. 2.ABO Research LaboratoryCairoEgypt
  3. 3.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  4. 4.Hannam UniversityKorea

Personalised recommendations