Multi-camera Tracking and Atypical Motion Detection with Behavioral Maps

  • Jérôme Berclaz
  • François Fleuret
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)


We introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different categories of random trajectories. Due to its simplicity, this model can be learned from video sequences in a totally unsupervised manner through an Expectation-Maximization procedure.

When integrated into a complete multi-camera tracking system, it improves the tracking performance in ambiguous situations, compared to a standard ad-hoc isotropic Markovian motion model. Moreover, it can be used to compute a score which characterizes atypical individual motions.

Experiments on outdoor video sequences demonstrate both the improvement of tracking performance when compared to a state-of-the-art tracking system and the reliability of the atypical motion detection.

Supplementary material

978-3-540-88690-7_9_MOESM1_ESM.avi (4.6 mb)
Supplementary material (4,665 KB)


  1. 1.
    Khan, S., Shah, M.: A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 267–282 (2007)CrossRefGoogle Scholar
  3. 3.
    Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Conference on Computer Vision and Pattern Recognition (2004)Google Scholar
  4. 4.
    Smith, K., Gatica-Perez, D., Odobez, J.M.: Using particles to track varying numbers of interacting people. In: Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  5. 5.
    Kang, J., Cohen, I., Medioni, G.: Tracking people in crowded scenes across multiple cameras. In: Asian Conference on Computer Vision (2004)Google Scholar
  6. 6.
    Oh, S., Russell, S., Sastry, S.: Markov chain monte carlo data association for general multiple-target tracking problems. In: IEEE Conference on Decision and Control, Paradise Island, Bahamas (2004)Google Scholar
  7. 7.
    Bui, H., Venkatesh, S., West, G.: Policy recognition in the abstract hidden markov models. Journal of Artificial Intelligence Research 17, 451–499 (2002)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Berclaz, J., Fleuret, F., Fua, P.: Pom: Probability occupancy map (2007),
  9. 9.
    Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: British Machine Vision Conference (1995)Google Scholar
  10. 10.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  11. 11.
    Bennewitz, M., Burgard, W., Cielniak, G.: Utilizing learned motion patterns to robustly track persons. In: Proceedings of the Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) (2003)Google Scholar
  12. 12.
    Makris, D., Ellis, T.: Learning semantic scene models from observing activity in visual surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(3), 397–408 (2005)CrossRefGoogle Scholar
  13. 13.
    Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(9), 1450–1464 (2006)CrossRefGoogle Scholar
  14. 14.
    Antonini, G., Venegas, S., Thiran, J.P., Bierlaire, M.: A discrete choice pedestrian behavior model for pedestrian detection in visual tracking systems. In: Advanced Concepts for Intelligent Vision Systems (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jérôme Berclaz
    • 1
  • François Fleuret
    • 2
  • Pascal Fua
    • 1
  1. 1.Computer Vision LaboratoryEPFLLausanneSwitzerland
  2. 2.IDIAP Research InstituteMartignySwitzerland

Personalised recommendations