Human Motion Tracking by Registering an Articulated Surface to 3D Points and Normals
- 25 April 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 31 (1), 158-163
- https://doi.org/10.1109/tpami.2008.108
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.This publication has 10 references indexed in Scilit:
- Human Motion Tracking with a Kinematic Parameterization of Extremal ContoursInternational Journal of Computer Vision, 2007
- Implicit Meshes for Effective Silhouette HandlingInternational Journal of Computer Vision, 2006
- Visual Shapes of Silhouette SetsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Shape-From-Silhouette Across Time Part II: Applications to Human Modeling and Markerless Motion TrackingInternational Journal of Computer Vision, 2005
- A hybrid approach for computing visual hulls of complex objectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Articulated soft objects for multiview shape and motion captureIEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
- Model-based multiple view reconstruction of peoplePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Model-Based Clustering, Discriminant Analysis, and Density EstimationJournal of the American Statistical Association, 2002
- Structural graph matching using the EM algorithm and singular value decompositionIeee Transactions On Pattern Analysis and Machine Intelligence, 2001
- Statistical Approaches to Feature-Based Object RecognitionInternational Journal of Computer Vision, 1997