Colour model selection and adaptation in dynamic scenes

  • Yogesh Raja
  • Stephen J. McKenna
  • Shaogang Gong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)


We use colour mixture models for real-time colour-based object localisation, tracking and segmentation in dynamic scenes. Within such a framework, we address the issues of model order selection, modelling scene background and model adaptation in time. Experimental results are given to demonstrate our approach in different scale and lighting conditions.


Mixture Model Gaussian Mixture Model Colour Model Colour Constancy Constructive Algorithm 
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.
    Y. Raja, S. McKenna, and S. Gong, “Segmentation and tracking using colour mixture models,” in Asian Conference on Computer Vision, Hong Kong, January 1998.Google Scholar
  2. 2.
    W. Skarbek and A. Koschan, “Colour image segmentation — a survey,” Tech. Rep., Tech. Univ. of Berlin, 1994.Google Scholar
  3. 3.
    S. McKenna, S. Gong, and Y. Raja, “Face recognition in dynamic scenes,” in BMVC, 1997.Google Scholar
  4. 4.
    J. Matas, R. Marik, and J. Kittler, “On representation and matching of multicoloured objects,” in ICCV, 1995, pp. 726–732.Google Scholar
  5. 5.
    M. J. Swain and D. H. Ballard, “Colour indexing,” IJCV, pp. 11–32, 1991.Google Scholar
  6. 6.
    C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.Google Scholar
  7. 7.
    B. S. Everitt and D. J. Hand, Finite Mixture Distributions, Chapman and Hall, New York, 1981.Google Scholar
  8. 8.
    G. J. McLachlan and K. E. Basford, Mixture Models: Inference and Applications to Clustering, Marcel Dekker Inc., New York, 1988.Google Scholar
  9. 9.
    C. E. Priebe, “Adaptive mixtures,” J. Amer. Stat. Assoc., vol. 89, no. 427, pp. 796–806, 1994.zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    C. E. Priebe and D. J. Marchette, “Adaptive mixtures: Recursive nonparametric pattern recognition,” Pattern Recognition, vol. 24, no. 12, pp. 1197–1209, 1991.CrossRefGoogle Scholar
  11. 11.
    C. E. Priebe and D. J. Marchette, “Adaptive mixture density estimation,” Pattern Recognition, vol. 26, no. 5, pp. 771–785, 1993.CrossRefGoogle Scholar
  12. 12.
    D. M. Titterington, A. F. M. Smith, and U. E. Makov, Statistical Analysis of Finite Mixture Distributions, John Wiley, New York, 1985.Google Scholar
  13. 13.
    S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation, 1992.Google Scholar
  14. 14.
    R. Kjeldsen and J. Render, “Finding skin in color images,” in 2nd Int. Conf. on Auto. Face and Gest. Recog., 1996.Google Scholar
  15. 15.
    D. Saxe and R. Foulds, “Toward robust skin identification in video images,” in 2nd Int. Conf. on Auto. Face and Gest. Recog., 1996.Google Scholar
  16. 16.
    B. Schiele and A. Waibel, “Gaze tracking based on face-color,” in IWAFGR, 1995, pp. 344–349.Google Scholar
  17. 17.
    H. Wu, Q. Chen, and M. Yachida, “An application of fuzzy theory: face detection,” in IWAFGR, Zurich, June 1995, pp. 314–319.Google Scholar
  18. 18.
    C.R. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, “Pfinder:real-time tracking of the human body,” IEEE PAMI, vol. 19, no. 7, pp. 780–785, 1997.Google Scholar
  19. 19.
    R. A. Redner and H. F. Walker, “Mixture densities, maximum likelihood and the EM algorithm,” SIAM Review, vol. 26, no. 2, pp. 195–239, 1984.zbMATHMathSciNetCrossRefGoogle Scholar
  20. 20.
    D. A. Forsyth, Colour Constancy and its Applications in Machine Vision, Ph.D. thesis, University of Oxford, 1988.Google Scholar
  21. 21.
    H. G. C. Traven, “A neural network approach to statistical pattern classification by “semiparametric” estimation of probability density functions,” IEEE Trans. Neural Networks, vol. 2, no. 3, pp. 366–378, 1991.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Yogesh Raja
    • 1
  • Stephen J. McKenna
    • 2
  • Shaogang Gong
    • 1
  1. 1.Department of Computer ScienceQueen Mary and Westfield CollegeEngland
  2. 2.Department of Applied ComputingUniversity of DundeeScotland

Personalised recommendations