Soccer Field Detection in Video Images Using Color and Spatial Coherence

  • Arnaud Le Troter
  • Sebastien Mavromatis
  • Jean Sequeira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3212)


We present an original approach based on the joint use of color and spatial coherence to automatically detect the soccer field in video sequences. We assume that the corresponding area is significant enough for that. This assumption is verified when the camera is oriented toward the field and does not focus on a given element of the scene such as a player or the ball. We do not have any assumption on the color of the field. We use this approach to automatically validate the image area in which the relevant scene elements are. This is a part of the SIMULFOOT project whose objective is the 3D reconstruction of the scene (players, referees, ball) and its animation as a support for cognitive studies and strategy analysis.


Video Sequence Color Space Video Image Spatial Coherence Discrete Representation 
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.
    Mavromatis, S., Baratgin, J., Sequeira, J.: Reconstruction and simulation of soccer sequences. In: MIRAGE 2003, Nice, France (2003)Google Scholar
  2. 2.
    Mavromatis, S., Baratgin, J., Sequeira, J.: Analyzing team sport strategies by means of graphical simulation. In: ICISP 2003, Agadir, Morroco (June 2003)Google Scholar
  3. 3.
    Ripoll, H.: Cognition and decision making in sport,International Perspectives on Sport and Exercise Psychology. In: Serpa, S., Alves, J., Pataco, V. (eds.) WV: Fitness Information technology, Inc., Morgantown, pp. 70–77 (1994)Google Scholar
  4. 4.
    Saitta, L., Zucker, J.D.: A model of abstraction in visual perception. Applied Artificial Intelligence 15, 761–776 (2001)CrossRefGoogle Scholar
  5. 5.
    Ericsson, K.A., Lehmann, A.C.: Expert and exceptional performance: Evidence of maximal adaptation to task constraints. Annual Review of Psychology 47, 273–305 (1996)CrossRefGoogle Scholar
  6. 6.
    Bebie, B.: Soccerman: reconstructing soccer games from video sequences. In: presented at IEEE International Conference on Image Processing, Chicago (1998)Google Scholar
  7. 7.
    Ohno, M., Shirai: Tracking players and estimation of the 3D position of a ball in soccer games. In: presented at IAPR International Conference on Pattern Recognition, Barcelona (2000)Google Scholar
  8. 8.
    Seo, C., Hong, K.: Where are the ball and players?: soccer game analysis with colorbased tracking and image mosaick. Presented at IAPR International Conference on Image Analysis and Processing, Florence (1997)Google Scholar
  9. 9.
    Carvalho, S., Gattas: Image-based Modeling Using a Two-step Camera calibration Method. In: presented at Proceedings of International Symposium on Computer Graphics, Image Processing and Vision, Rio de Janeiro (1998)Google Scholar
  10. 10.
    Amer, A., Mitiche, A., Dubois, E.: Context independent real-time event recognition: application to key-image extraction. In: International Conference on Pattern Recognition Québec Canada (August 2002)Google Scholar
  11. 11.
    Lefvre, S., Mercier, L., Tiberghien, V., Vincent, N.: Multiresolution color image segmentation applied to background extraction in outdoor images. In: IST European Conference on Color in Graphics, Image and Vision, Poitiers, France, April 2002, pp. 363–367 (2002)Google Scholar
  12. 12.
    Vandenbroucke, N., Macaire, L., Postaire, J.: Color pixels classification in an hybrid color space. In: IEEE International conference on Image Processing, Chicago, pp. 176–180 (1998)Google Scholar
  13. 13.
    Carron, Segmentation d’images couleur dans la base Teinte Luminance Saturation: approche numérique et symbolique. Université de Savoie (1995)Google Scholar
  14. 14.
    Vandenbroucke, N., Macaire, L., Postaire, J.: Segmentation d’images couleur par classification de pixels dans des espaces d’attributs colorimétriques adaptés: Application l’analyse d’images de football. pp. 238, Université des Sciences et Technologies de Lille, Lille (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Arnaud Le Troter
    • 1
  • Sebastien Mavromatis
    • 1
  • Jean Sequeira
    • 1
  1. 1.LSIS Laboratory LXAO groupUniversity of MarseillesFrance

Personalised recommendations