Estimation of articulated motion using kinematically constrained mixture densities

We address the problem of articulated posture estimation in its general form. Namely, the recovery of full 3D articulated posture parameters from an uncontrolled scene. Stochastic modeling of low-level segmented image data is unified with models of object kinematic structure through a constrained mixture of observation processes. A modified expectation-maximization algorithm is proposed for this purpose. Early experiments qualitatively demonstrate the efficacy of our approach, and provide a context for integration for more sophisticated image cues.