Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers

Filtering based algorithms have become popular in tracking human body pose. Such algorithms can suffer the curse of dimensionality due to the high dimensionality of the pose state space; therefore, efforts have been dedicated to either smart sampling or reducing the dimensionality of the original pose state space. In this paper, a novel formulation that employs a dimensionality reduced state space for multi-hypothesis tracking is proposed. During off-line training, a mixture of factor analyzers is learned. Each factor analyzer can be thought of as a "local dimensionality reducer" that locally approximates the pose manifold. Global coordination between local factor analyzers is achieved by learning a set of linear mixture functions that enforces agreement between local factor analyzers. The formulation allows easy bidirectional mapping between the original body pose space and the low-dimensional space. During online tracking, the clusters of factor analyzers are utilized in a ...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Rui Li, Ming-Hsuan Yang, Stan Sclaroff, Tai-Peng Tian
• Keywords: Body Pose Space, Computer Vision, ECCV 2006, Human Body Pose, Local Factor Analyzers, Original Pose State, Pose State Space