Learning Image Statistics for Bayesian Tracking

(with Hedvig Sidenbladh)

This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects in image sequences. We focus on the probabilistic tracking of people and learn models of how they appear and move in images. In particular, we learn the likelihood of observing various spatial and temporal filter responses corresponding to edges, ridges, and motion differences given a model of the person. Similarly, we learn probability distributions over filter responses for general scenes that define a likelihood of observing the filter responses for arbitrary backgrounds. We then derive a probabilistic model for tracking that exploits the ratio between the likelihood that image pixels corresponding to the foreground (person) were generated by an actual person or by some unknown background. The paper extends previous work on learning image statistics and combines it with Bayesian tracking using particle filtering. By combining multiple image cues, and by using learned likelihood models, we demonstrate improved robustness and accuracy when tracking complex objects such as people in monocular image sequences with cluttered scenes and a moving camera.

For more information and results on 3D human tracking (click here).

Image sequences and learned models of filter responses are available for download (click here).

Related Publications

Learning image statistics for Bayesian tracking,
Sidenbladh, H. and Black, M. J.,
Int. Conf. on Computer Vision, ICCV-2001, Vancouver, BC, Vol. II, pp. 709-716.
(postscript, 2.8MB)(pdf, 0.38MB).

Implicit probabilistic models of human motion for synthesis and tracking,
Sidenbladh, H., Black, M. J., and Sigal, L.,
to appear: European Conf. on Computer Vision, ECCV2002.
(abstract), (postscript), (pdf).

Learning and tracking cyclic human motion,
Ormoneit, D., Sidenbladh, S., Black, M. J., Hastie, T.,
Advances in Neural Information Processing Systems 13, Leen, Todd K. and Dietterich, Thomas G. and Tresp, Volker, Eds., The MIT Press, pp. 894-900, 2001.
(abstract), (pdf), (ps.gz).

Stochastic tracking of 3D human figures using 2D image motion,
Sidenbladh, H., Black, M. J., and Fleet, D.J.,
European Conference on Computer Vision, D. Vernon (Ed.), Springer Verlag, LNCS 1843, Dublin, Ireland, pp. 702-718 June 2000.
(postscript)(pdf), (abstract)

A framework for modeling the appearance of 3D articulated figures,
Sidenbladh, H., De la Torre, F., Black, M. J.,
Int. Conf. on Automatic Face and Gesture Recognition, Grenoble, France, pp. 368-375, March 2000.
(postscript), (abstract)