Disclosed is a method and system for efficiently and accurately tracking three-dimensional (3D) human motion from a two-dimensional (2D) video sequence, even when self-occlusion, motion blur and large limb movements occur. In an offline learning stage, 3D motion capture data is acquired and a prediction model is generated based on the learned motions. A mixture of factor analyzers acts as local dimensionality reducers. Clusters of factor analyzers formed within a globally coordinated low-dimensional space makes it possible to perform multiple hypothesis tracking based on the distribution modes. In the online tracking stage, 3D tracking is performed without requiring any special equipment, clothing, or markers. Instead, motion is tracked in the dimensionality reduced state based on a monocular video sequence.
RELATED APPLICATIONS
This application claims priority from U.S. provisional application No. 60/731,399 filed Oct. 28, 2005 entitled "Monocular Tracking of 3D Human Motion With a Coordinated Mixture of Factor Analyzers" which is incorporated by reference herein in their entirety.
