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.
Estimation of articulated motion using kinematically constrained mixture densities
1997-01-01
898230 byte
Conference paper
Electronic Resource
English
Estimation of Articulated Motion Using Kinematically Constrained Mixture Densities
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