title: Human motion tracking by registering an articulated surface to 3D 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 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.
authors.affiliation: INRIA Grenoble-Rhone-Alpes, Montbonnot Saint-Martin, France. Radu.Horaud@inrialpes.fr
authors.forename: Radu
authors.initials: R
authors.lastname: Horaud
authors.forename: Matti
authors.initials: M
authors.lastname: Niskanen
authors.forename: Guillaume
authors.initials: G
authors.lastname: Dewaele
authors.forename: Edmond
authors.initials: E
authors.lastname: Boyer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007596:Joints / Q000033:anatomy & histology / Q000502:physiology*; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*