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

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4 Author(s)
Horaud, R. ; INRIA Grenoble-Rhone-Alpes, Montonnot St. Martin ; Niskanen, M. ; Dewaele, G. ; Boyer, E.

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 an articulated object, as well as probabilities that the data are assigned either to an object 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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:31 ,  Issue: 1 )