Feature Points Tracking: Robustness to Specular Highlights and Lighting Changes

  • Michèle Gouiffès
  • Christophe Collewet
  • Christine Fernandez-Maloigne
  • Alain Trémeau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)


Since the precise modeling of reflection is a difficult task, most feature points trackers assume that objects are lambertian and that no lighting change occurs. To some extent, a few approaches answer these issues by computing an affine photometric model or by achieving a photometric normalization. Through a study based on specular reflection models, we explain explicitly the assumptions on which these techniques are based. Then we propose a tracker that compensates for specular highlights and lighting variations more efficiently when small windows of interest are considered. Experimental results on image sequences prove the robustness and the accuracy of this technique in comparison with the existing trackers. Moreover, the computation time of the tracking is not significantly increased.


Tracking Method Small Window Illumination Change Feature Tracking Lighting 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.


  1. 1.
    Espiau, F.X., Malis, E., Rives, P.: Robust features tracking for robotic applications: towards 2d1/2 visual servoing with natural images. In: IEEE Int. Conf. on Robotics and Automation, ICRA 2002, Washington, USA (May 2002)Google Scholar
  2. 2.
    Hager, G.D., Belhumeur, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(10), 1025–1039 (1998)CrossRefGoogle Scholar
  3. 3.
    Horn, K.P., Schunck, B.G.: Determinig optical flow. Artificial Intelligence 7, 185–203 (1981)CrossRefGoogle Scholar
  4. 4.
    Jin, H., Soatto, S., Favaro, P.: Real-time feature tracking and outlier rejection with changes in illumination. In: IEEE Int. Conf. on Computer Vision, Vancouver, Canada, July 9-12, 2001, pp. 684–689 (2001)Google Scholar
  5. 5.
    Lucas, B.D., Kanade, T.: An iterative image registration technique. In: IJCAI 1981, Vancouver, British Columbia, August 1981, pp. 674–679 (1981)Google Scholar
  6. 6.
    Negahdaripour, S.: Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(9), 961–979 (1998)CrossRefGoogle Scholar
  7. 7.
    Phong, B.-T.: Illumination for computer generated images. Communications of the ACM 18(6), 311–317 (1975)CrossRefGoogle Scholar
  8. 8.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, CVPR 1994, Seattle, Washington, USA, June 1994, pp. 593–600 (1994)Google Scholar
  9. 9.
    Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report CMU-CS-91-132, Carnegie Mellon University (April 1991)Google Scholar
  10. 10.
    Tommasini, T., Fusiello, A., Trucco, E., Roberto, V.: Improving feature tracking with robust statistics. Pattern Analysis & Applications 2, 312–320 (1999)CrossRefGoogle Scholar
  11. 11.
    Torrance, K.E., Sparrow, E.M.: Theory for off-specular reflection from roughened surfaces. Journal of the Optical Society of America 57(9) (September 1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michèle Gouiffès
    • 1
  • Christophe Collewet
    • 2
  • Christine Fernandez-Maloigne
    • 1
  • Alain Trémeau
    • 3
  1. 1.SICUniversity of PoitiersFuturoscopeFrance
  2. 2.IRISA/INRIA RennesRennesFrance
  3. 3.LIGIVUniversity Jean MonnetSaint EtienneFrance

Personalised recommendations