2008 IEEE Conference on Computer Vision and Pattern Recognition (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
Marek Vondrak , Brown University, USA
Leonid Sigal , University of Toronto, USA
Odest Chadwicke Jenkins , Brown University, USA
Human motion tracking is an important problem in computer vision. Most prior approaches have concentrated on efficient inference algorithms and prior motion models; however, few can explicitly account for physical plausibility of recovered motion. The primary purpose of this work is to enforce physical plausibility in the tracking of a single articulated human subject. Towards this end, we propose a full-body 3D physical simulation-based prior that explicitly incorporates motion control and dynamics into the Bayesian filtering framework. We consider the human’s motion to be generated by a “control loop”. In this control loop, Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of forces. Collisions generate interaction forces to prevent physically impossible hypotheses. This allows us to properly model human motion dynamics, ground contact and environment interactions. For efficient inference in the resulting high-dimensional state space, we introduce exemplar-based control strategy to reduce the effective search space. As a result we are able to recover the physically-plausible kinematic and dynamic state of the body from monocular and multi-view imagery. We show, both quantitatively and qualitatively, that our approach performs favorably with respect to standard Bayesian filtering methods.
O. C. Jenkins, L. Sigal and M. Vondrak, "Physical simulation for probabilistic motion tracking," 2008 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Anchorage, AK, USA, 2008, pp. 1-8.