Sequential Probabilistic Grass Field Segmentation of Soccer Video Images

  • Kaveh Kangarloo
  • Ehsanollah Kabir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)


In this paper, we present a method for segmentation of grass field of soccer video images. Since the grass field is observed as a green and nearly soft region, the hue and a feature representing the color dispersion in horizontal and vertical directions are used to model the grass field as a mixture of Gaussian components. At first, the grass field is roughly segmented. On the base of grass field model, the probability density function of non-grass field is estimated. Finally using the Bayes theory, in a recurrent process the grass field is finally segmented.


Football Grass-Field Color Texture Gaussian Mixture Model Bayes theory Segmentation 


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  1. 1.
    Pal, N.R., Pal, S.K.: A Review on Image Segmentation Techniques. Pattern Recognition Letters 26(9), 1277–1294 (1993)Google Scholar
  2. 2.
    Southall, B., Buxton, B., Marchant, J., Hague, T.: On the Performance Characterization of Image Segmentation Algorithms: A Case Study. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 351–365. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Stiller, C., Konrad, J.: Estimating motion in image sequences. IEEE Signal Processing Magazine 16, 70–91 (1999)CrossRefGoogle Scholar
  4. 4.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Fraley, C., Raftery, A.E.: How many clusters? Which clustering method? Answers via model-based cluster analysis. Technical Report 329, Department of Statistic University of Washington (1998)Google Scholar
  6. 6.
    Jianbo, S., Malik, J.: Normalized cuts and image segmentation. IEEE Transaction on Pattern analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  7. 7.
    Seo, Y., Choi, S., Kim, H., Hong, K.S.: Where are the ball and players?: Soccer Game Analysis with Color-based Tracking and Image Mosaic. In: Proceedings of Int. Conference on Image Analysis and Processing (ICIAP), pp. 196–203 (1997)Google Scholar
  8. 8.
    Utsumi, O., Miura, K., Ide, I., Sakai, S., Tanaka, H.: An Object Detection Method for Describing Soccer Games from Video. In: Proceedings of IEEE Int. Conference on Multimedia and Expo (ICME), vol. 1, pp. 45–48 (2002)Google Scholar
  9. 9.
    Sudhir, G., Lee, J.C.M., Jain, A.K.: Automatic Classification of Tennis Video for High-level Content-based Retrieval. In: International Workshop on Content-Based Access of Image and Video Databases (CAIVD), pp. 81–90 (1998)Google Scholar
  10. 10.
    Hua, W., Han, M., Gong, Y.: Baseball Scene Classification Using Multimedia Features. In: Proceedings of IEEE International Conference on Multimedia and Expo., vol. 1, pp. 821–824 (2002)Google Scholar
  11. 11.
    Xu, P., Xie, L., Chang, S.F., Divakaran, A., Vetro, A., Sun, H.: Algorithms and System for Segmentation and Structure Analysis in Soccer Video. In: Proceedings of IEEE Int. Conference on Multimedia and Expo (ICME), pp. 928–931 (2001)Google Scholar
  12. 12.
    Raja, Y., McKenna, S., Gong, S.: Color model selection and adaptation in dynamic scenes. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 460–474. Springer, Heidelberg (1998)Google Scholar
  13. 13.
    Liu, J.G., Moore, J.M.: Hue image RGB color composition. A simple technique to suppress shadow and enhance spectral signature. International Journal of Remote Sensing 1, 1521–1530 (1990)CrossRefGoogle Scholar
  14. 14.
    Materka, A., Strzelecki, M.: Texture analysis methods – A review. Technical Report, University of Lodz, Institute of Electronics, COST B11 Technical Report, Brussels (1998)Google Scholar
  15. 15.
    Williams, C.K.I., Barber, D.: Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1342–1352 (1998)CrossRefGoogle Scholar
  16. 16.
    McLachlan, Krishnan, T.: The EM algorithm and extensions. Wiley, Chichester (1997)zbMATHGoogle Scholar
  17. 17.
    ISO-13818-2: Generic Coding of moving pictures and associated audio (MPEG-2) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kaveh Kangarloo
    • 1
    • 2
  • Ehsanollah Kabir
    • 3
  1. 1.Dept. of Electrical Eng.Azad University, Central Tehran BranchTehranIran
  2. 2.Dept. of Electrical Eng.Azad University, Science and Research unitTehranIran
  3. 3.Dept. of Electrical Eng.Tarbiat Modarres UniversityTehranIran

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