Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers

  • Authors:

  • Rui Li;Ming-Hsuan Yang;Stan Sclaroff;Tai-Peng Tian

  • Affiliations:

  • Boston University, Boston, MA;Honda Research Institute, Mountain View, CA;Boston University, Boston, MA;Boston University, Boston, MA

  • Venue:
  • ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 multiple hypothesis tracking algorithm. Experiments demonstrate that the proposed algorithm tracks 3D body pose efficiently and accurately , even when self-occlusion, motion blur and large limb movements occur. Quantitative comparisons show that the formulation produces more accurate 3D pose estimates over time than those that can be obtained via a number of previously-proposed particle filtering based tracking algorithms.