MONOCULAR TRACKING OF 3D HUMAN MOTION WITH A COORDINATED MIXTURE OF FACTOR ANALYZERS

摘要

Disclosed is a method and system for efficiently and accurately tracking three-dimensional (3D) human motion (302) from a two- dimensional (2D) video sequence (324), even when self-occlusion, motion blur and large limb movement occur. In an offline learning stage, 3D motion capture data is acquired and a prediction model (326) 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.