Real-Time Adaptive Colour Segmentation for the RoboCup Middle Size League

  • Claudia Gönner
  • Martin Rous
  • Karl-Friedrich Kraiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)


In order to detect objects using colour information, the mapping from points in colour space to the most likely object must be known. This work proposes an adaptive colour calibration based on the Bayes Theorem and chrominance histograms. Furthermore the object’s shape is considered resulting in a more robust classification. A randomised hough transform is employed for the ball. The lines of the goals and flagposts are extracted by an orthogonal regression. Shape detection corrects over- and undersegmentations of the colour segmentation, thus enabling an update of the chrominance histograms. The entire algorithm, including a segmentation and a recalibration step, is robust enough to be used during a RoboCup game and runs in real-time.


  1. 1.
    Bruce, J., Balch, T.: Fast and inexpensive color image segmentation for interactive robots. In: IROS 2000, pp. 2061–2066 (2000)Google Scholar
  2. 2.
    Brusey, J., Padgham, L.: Techniques for obtaining robust, real-time, colour-based vision for robotics. In: Veloso, M.M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 243–256. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Amorosco, C., Chella, A., Morreale, V., Storniolo, P.: A segmentation system for soccer robot based on neural networks. In: Veloso, M.M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 136–147. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Mayer, G., Utz, H., Kraetzschmar, G.: Toward autonomous vision self-calibration for soccer robots. In: IROS 2002, pp. 214–219 (2002)Google Scholar
  5. 5.
    Cameron, D., Barnes, N.: Knowledge-based autonomous dynamic color calibration. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 226–237. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Swain, M., Ballard, D.: Color indexing. Intl. J. of Computer Vision 7, 11–32 (1991)CrossRefGoogle Scholar
  7. 7.
    Raja, Y., McKenna, S., Gong, S.: Tracking and segmenting people in varying lighting conditions using color. In: 3rd Int. Conf. on Face and Gesture Recognition, Nara, Japan, pp. 228–233 (1998)Google Scholar
  8. 8.
    Dahm, I., Deutsch, S., Hebbel, M., Osterhues, A.: Robust color classification for robot soccer. In: Robocup 2003, Padua, Italy (2003)Google Scholar
  9. 9.
    Jonker, P., Caarls, J., Bokhove, W.: Fast and accurate robot vision for vision based motion. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 149–158. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Hanek, R., Schmitt, T., Buck, S., Beetz, M.: Fast image-based object localization in natural scenes. In: IROS 2002, pp. 116–122 (2002)Google Scholar
  11. 11.
    Hanek, R., Schmitt, T., Buck, S., Beetz, M.: Towards robocup without color labeling. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 179–194. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Gönner, C., Rous, M., Kraiss, K.F.: Robuste farbbasierte Bildsegmentierung für mobile Roboter. In: Autonome Mobile Systeme, Karlsruhe, Germany, pp. 64–74. Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Bandlow, T., Klupsch, M., Hanek, R., Schmitt, T.: Fast image segmentation, object recognition and localization in a robocup scenario. In: Veloso, M.M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 174–185. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    McLaughlin, R.: Randomized hough transform: Improved ellipse detection with comparison. Pattern Rocognition Letters 19, 299–305 (1998)zbMATHCrossRefGoogle Scholar
  15. 15.
    Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized hough transform (RHT). Pattern Rocognition Letters 11, 331–338 (1990)zbMATHCrossRefGoogle Scholar
  16. 16.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley and Sons, Chichester (1973)zbMATHGoogle Scholar
  17. 17.
    LTI-Lib: C++ computer vision library

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Claudia Gönner
    • 1
  • Martin Rous
    • 1
  • Karl-Friedrich Kraiss
    • 1
  1. 1.Chair of Technical Computer ScienceTechnical University of Aachen (RWTH)Aachen

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