Advertisement

Learning the Scene Illumination for Color-Based People Tracking in Dynamic Environment

  • Sinan Mutlu
  • Tao Hu
  • Oswald Lanz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

People tracking under non-uniform illumination is challenging, as observed appearance may change as they move around in the environment. Appearance model adaptation is inconvenient over the long run as it is subject to drift, while filtering illumination information in the data through built-in invariance is sub-optimal in terms of discriminative capability. In this work, we are interested in modeling the spatial and temporal dimensions of appearance variation induced by non-uniform illumination, and to learn and adapt related parameters over time by using walking people as illumination probes. We propose a hybrid graphical model and a new message passing scheme that sequentially updates parameters of the model, so that scene illumination can be learnt online and used for robust tracking in dynamic environment.

Keywords

tracking online learning belief propagation message passing particle filter illumination 

References

  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38 (2006)Google Scholar
  2. 2.
    Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. Eurasip. J. Adv. Signal Process. 43 (2010)Google Scholar
  3. 3.
    Vezzani, R., Grana, C., Cucchiara, R.: Probabilistic people tracking with appearance models and occlusion classification: The AD-HOC system. Pattern Recogn. Lett. 32, 6 (2011)CrossRefGoogle Scholar
  4. 4.
    Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Patt. Anal. Mach. Intell. 25, 10 (2003)Google Scholar
  5. 5.
    Tsai, Y.P., Ko, C.H., Hung, Y.P., Shih, Z.C.: Background removal of multiview images by learning shape priors. IEEE Trans. Image Proc. 16, 10 (2007)MathSciNetGoogle Scholar
  6. 6.
    Camplani, M., Luis Salgado, L.: Adaptive background modeling in multicamera system for real-time object detection. SPIE Optical Eng. 50, 12 (2011)Google Scholar
  7. 7.
    Zen, G., Lanz, O., Messelodi, S., Ricci, E.: Tracking Multiple People with Illumination Maps. In: Proc. ICPR (2010)Google Scholar
  8. 8.
    Bardet, F., Chateau, T., Ramadasan, D.: Illumination aware MCMC Particle Filter for long-term outdoor multi-object simultaneous tracking and classification. In: Proc. ICCV (2009)Google Scholar
  9. 9.
    Lanz, O., Messelodi, S.: A Sampling Algorithm for Occlusion Robust Multi Target Detection. In: Proc. AVSS (2009)Google Scholar
  10. 10.
    Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding Belief Propagation and Its Generalizations. MERL tech. rep. (2003)Google Scholar
  11. 11.
    Lecca, M., Messelodi, S.: Linking the von Kries model to Wien’s law for the estimation of an illuminant invariant image. Pattern Recogn. Lett. 32(15) (2011)Google Scholar
  12. 12.
    Lanz, O.: Approximate bayesian multibody tracking. IEEE Trans. Patt. Anal. Mach. Intell. 28(9) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sinan Mutlu
    • 1
    • 2
  • Tao Hu
    • 1
    • 2
  • Oswald Lanz
    • 1
  1. 1.FBK Fondazione Bruno KesslerPovoItaly
  2. 2.ICT Doctoral SchoolUniversity of TrentoPovoItaly

Personalised recommendations