A Dynamic Bayesian Network Approach to Figure Tracking using Learned Dynamic Models

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed either simple, generic dynamic models or highly specific hand-tailored ones. Recently, a broad class of learning and inference algorithms for time-series models have been successfully cast in the framework of dynamic Bayesian networks (DBNs). This paper describes a novel DBN-based switching linear dynamic system (SLDS) model and presents its application to figure motion analysis. A key feature of our approach is an approximate Viterbi inference technique for overcoming the intractability of exact inference in mixed-state DBNs. We present experimental results for learning figure dynamics from video data and show promising initial results for tracking, interpolation, synthesis, and classification using learned models.

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham, Kevin P. Murphy
• Keywords: Approximate Viterbi Inference, Computer Vision, Dynamic Bayesian Networks, Figure Motion, Generic Dynamic Models, Human Figure Exhibits, ICCV 1999