SIFT Based Ball Recognition in Soccer Images

  • Marco Leo
  • Tiziana D’Orazio
  • Paolo Spagnolo
  • Pier Luigi Mazzeo
  • Arcangelo Distante
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

In this paper a new method for ball recognition in soccer images is proposed. It combines Circular Hough Transform and Scale Invariant Feature Transform to recognize the ball in each acquired frame. The method is invariant to image scale, rotation, affine distortion, noise and changes in illumination. Compared with classical supervised approaches, it is not necessary to build different positive training sets to properly manage the great variance in ball appearances. Moreover, it does not require the construction of negative training sets that, in a context as soccer matches where many no-ball examples can be found, it can be a tedious and long work. The proposed approach has been tested on a number of image sequences acquired during real matches of the Italian Soccer “Serie A” championship. Experimental results demonstrate a satisfactory capability of the proposed approach to recognize the ball.

Keywords

Scale Invariant Feature Transform Evening Match Scale Invariant Feature Transform Feature Ball Image Keypoint Match 
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.

References

  1. 1.
    Tong, X.-F., Lu, H.-Q., Liu, Q.-S.: An effective and fast soccer ball detection and tracking method, Pattern Recognition, 2004. In: ICPR 2004. Proceedings of the 17th International Conference, August 23-26, 2004, vol. 4, pp. 795–798 (2004)Google Scholar
  2. 2.
    Yu, X., Leong, H.W., Xu, C., Tian, Q.: Trajectory-based ball detection and tracking in broadcast soccer video. IEEE Transactions on Multimedia 8(6), 1164–1178 (2006)CrossRefGoogle Scholar
  3. 3.
    Ren, J., Orwell, J., Jones, G.A., Xu, M.: A general framework for 3d soccer ball estimation and tracking. In: ICIP 2004, pp. 1935–1938 (2004)Google Scholar
  4. 4.
    D’Orazio, T., Guaragnella, C., Leo, M., Distante, A.: A new algorithm for ball recognition using circle Hough Transform and neural classifier. Pattern Recognition 37, 393–408 (2004)CrossRefGoogle Scholar
  5. 5.
    Leo, M., DOrazio, T., Distante, A.: Independent Component Analysis for Ball Recognition in Soccer Images. In: Proceeding of the Intelligent Systems and Control ~ISC 2003, Salzburg, Austria (2003)Google Scholar
  6. 6.
    Murase, H.: Visual Learning and Recognition of 3-D Objects from Appearance. International Journal of Computer Vision 14, 5–24 (1995)CrossRefGoogle Scholar
  7. 7.
    Papageorgiou, C., Oren, M., Poggio, T.: A general framework for Object Detection. In: Proc. of Intern Conference for Computer Vision (January 1998)Google Scholar
  8. 8.
    Rowley, H., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Trans. On Pattern analysis and Machine Intelligence 20(1), 23–38 (1998)CrossRefGoogle Scholar
  9. 9.
    Jones, M., Poggio, T.: Mode- based Matching by Linear Combinations of Prototypes. In: Proc. of Image Understanding workshop (1997)Google Scholar
  10. 10.
    Mohan, A., Papageorgoiu, C., Poggio, T.: Example-based Object Detection in Images by Components. IEEE Trans. On Pattern analysis and Machine Intelligence 23(4), 349–361 (2001)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Atherton, T.J., Kerbyson, d.J.: Size invariant circle detection. Image and video Computing 17, 795–803 (1999)MATHCrossRefGoogle Scholar
  13. 13.
    Matsumoto, K., Sudo, S., Saito, H., Ozawa, S.: Optimized Camera Viewpoint Determination System for Soccer Game Broadcasting. In: Seo, Y., Choi, S., Kim, H., Hong, K.S. (eds.) Proc. IAPR Workshop on Machine Vision Applications, Tokyo, pp. 115–118 (2000)Google Scholar
  14. 14.
    Ancona, N., Cicirelli, G., Branca, A., Distante, A.: Ball recognition in images for detecting goal in football. In: ANNIE 2001, St. Louis, MO (November 2001)Google Scholar
  15. 15.
    Ancona, N., Cicirelli, G., Branca, A., Distante, A.: Goal detection in football by using support vector machine for classification. In: International Joint INNS–IEEE Conference on Neural Network, Washington, DC (July 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marco Leo
    • 1
  • Tiziana D’Orazio
    • 1
  • Paolo Spagnolo
    • 1
  • Pier Luigi Mazzeo
    • 1
  • Arcangelo Distante
    • 1
  1. 1.Institute of Intelligent Systems for Automation BariItaly

Personalised recommendations