Abstract
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.
Chapter PDF
Similar content being viewed by others
References
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)
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)
Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Conference on Computer Vision and Pattern Recognition (2004)
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)
Kang, J., Cohen, I., Medioni, G.: Tracking people in crowded scenes across multiple cameras. In: Asian Conference on Computer Vision (2004)
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)
Bui, H., Venkatesh, S., West, G.: Policy recognition in the abstract hidden markov models. Journal of Artificial Intelligence Research 17, 451–499 (2002)
Berclaz, J., Fleuret, F., Fua, P.: Pom: Probability occupancy map (2007), http://cvlab.epfl.ch/software/pom/index.php
Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: British Machine Vision Conference (1995)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Electronic Supplementary Material
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berclaz, J., Fleuret, F., Fua, P. (2008). Multi-camera Tracking and Atypical Motion Detection with Behavioral Maps. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88690-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-88690-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88689-1
Online ISBN: 978-3-540-88690-7
eBook Packages: Computer ScienceComputer Science (R0)