Tracking Articulated Hand Motion with Eigen Dynamics Analysis

This paper introduces the concept of eigen-dynamics and proposes an eigen dynamics analysis (EDA) method to learn the dynamics of natural hand motion from labelled sets of motion captured with a data glove. The result is parameterized with a high-order stochastic linear dynamic system (LDS) consisting of five lower-order LDS. Each corresponding to one eigen-dynamics. Based on the EDA model, we construct a dynamic Bayesian network (DBN) to analyze the generative process of a image sequence of natural hand motion. Using the DBN, a hand tracking system is implemented. Experiments on both synthesized and real-world data demonstrate the robustness and effectiveness of these techniques.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Hanning Zhou, Thomas S. Huang
• Keywords: Computer Vision, Eigen Dynamics Analysis, ICCV 2003, Natural Hand Motion, five Lower-order Lds