A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models

Switching linear dynamic systems (SLDS) attempt to describe a complex nonlinear dynamic system with a succession of linear models indexed by a switching variable. Unfortunately, despite SLDS's simplicity exact state and parameter estimation are still intractable. 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 SLDS model. A key feature of our approach are two approximate inference techniques for overcoming the intractability of exact inference in SLDS. As an example, we apply our model to the human figure motion analysis. We present experimental results for learning figure dynamics from video data and show promising results for tracking, interpolation, synthesis, and classification using learned models.

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