Comparison of Techniques for Mitigating the Effects of Illumination Variations on the Appearance of Human Targets

  • C. Madden
  • M. Piccardi
  • S. Zuffi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


Several techniques have been proposed to date to build colour invariants between camera views with varying illumination conditions. In this paper, we propose to improve colour invariance by using data-dependent techniques. To this aim, we compare the effectiveness of histogram stretching, illumination filtration, full histogram equalisation and controlled histogram equalisation in a video surveillance domain. All such techniques have limited computational requirements and are therefore suitable for real time implementation. Controlled histogram equalisation is a modified histogram equalisation operating under the influence of a control parameter [1]. Our empirical comparison looks at the ability of these techniques to make the global colour appearance of single human targets more matchable under illumination changes, whilst still discriminating between different people. Tests are conducted on the appearance of individuals from two camera views with greatly differing illumination conditions and invariance is evaluated through a similarity measure based upon colour histograms. In general, our results indicate that these techniques improve colour invariance; amongst them, full and controlled equalisation consistently showed the best performance.


Colour Histogram Illumination Change Histogram Equalisation Camera View Illumination Variation 
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.
    Madden, C., Cheng, E.D., Piccardi, M.: Tracking people across disjoint camera views by an illumination-tolerant appearance representation. Machine Vision and Applications 18, 233–247 (2007)zbMATHCrossRefGoogle Scholar
  2. 2.
    Abdel-Hakim, A.E., Farag, A.A.: Csift: A sift descriptor with color invariant characteristics. International Conference on Computer Vision and Pattern Recognition 2, 1978–1983 (2006)Google Scholar
  3. 3.
    Finlayson, G., Hordley, S., Schaefer, G., Tian, G.Y.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognition 38, 179–190 (2005)CrossRefGoogle Scholar
  4. 4.
    Barnard, K., Funt, B.: Camera characterization for color research. Color Research and Application 27, 153–164 (2002)Google Scholar
  5. 5.
    Bala, R.: Device characterization. In: Sharma, G. (ed.) Digital Color Imaging Handbook, CRC Press, Boca Raton, USA (2003)Google Scholar
  6. 6.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. IEEE Conference on Computer Vision and Pattern Recognition 2, 26–33 (2005)Google Scholar
  7. 7.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. International Conference on Computer Vision 2, 952–957 (2003)CrossRefGoogle Scholar
  8. 8.
    Weiss, Y.: Deriving intrinsic images from image sequences. International Conference on Computer Vision 2, 68–75 (2001)Google Scholar
  9. 9.
    Barnard, K., Funt, B., Cardei, V.: A comparison of computational colour constancy algorithms; part one: Methodology and experiments with synthesized data. IEEE Transactions in Image Processing 11, 972–984 (2002)CrossRefGoogle Scholar
  10. 10.
    Toth, D., Aach, T., Metzler, V.: Bayesian spatiotemporal motion detection under varying illumination. In: European Signal Processing Conference pp. 2081–2084 (2000)Google Scholar
  11. 11.
    Zhou, S.K., Chellapa, R.: From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel hilbert space. IEEE Transactions on Pattern Analysis And Machine Intelligence 28, 917–929 (2006)CrossRefGoogle Scholar
  12. 12.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis And Machine Intelligence 19(7), 780–785 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • C. Madden
    • 1
  • M. Piccardi
    • 1
  • S. Zuffi
    • 2
  1. 1.University of Technology, SydneyAustralia
  2. 2.ITC-CNR, MilanoItaly

Personalised recommendations