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arXiv · 2012.04514

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

Radu Horaud·Matti Niskanen·Guillaume Dewaele·Edmond Boyer
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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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Radu Horaud, Matti Niskanen, Guillaume Dewaele, Edmond Boyer. 2020-12-08. Human Motion Tracking by Registering an Articulated Surface to 3-D Points and Normals. https://doi.org/10.1109/tpami.2008.108

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