Human Motion Tracking by Registering an Articulated Surface to 3D Points and Normals
We address the problem of human motion tracking by registering a surface to 3D 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 class. 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 (3D surface points and normals) gathered from imperfect silhouettes.
model-based tracking human motion capture articulated implicit surface shape from silhouettes robust surface registration expectation-maximization