Human Motion Tracking by Registering an Articulated Surface to 3-D Points and Normals

Abstract

We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of a kinematic human-body representation, as well as probabilities that the data are assigned either to a body part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes.

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• Rigid and Articulated Point Registration with Expectation Conditional Maximization
• Estimation of Human Body Shape and Posture Under Clothing
• A Data-driven Approach for Human Pose Tracking Based on Spatio-temporal Pictorial Structure
• A generalized parametric 3D shape representation for articulated pose estimation