Stochastic Tracking of 3D Human Figures Using 2D Image Motion

(with Hedvig Sidenbladh and David Fleet)

A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appearance, a robust likelihood function based on image graylevel differences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior probability distribution over model parameters is represented using a discrete set of samples and is propagated over time using particle filtering. The approach extends previous work on parameterized optical flow estimation to exploit a complex 3D articulated motion model. It also extends previous work on human motion tracking by including a perspective camera model, by modeling limb self occlusion, and by recovering 3D motion from a monocular sequence. The explicit posterior probability distribution represents ambiguities due to image matching, model singularities, and perspective projection. The method relies only on a frame-to-frame assumption of brightness constancy and hence is able to track people under changing viewpoints, in grayscale image sequences, and with complex unknown backgrounds.

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

Related Publications

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

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

Ormoneit, D., Sidenbladh, S., Black, M. J., Hastie, T., Learning and tracking cyclic human motion, 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).

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