Combining simple models to approximate complex dynamics

Abstract

Stochastic tracking of structured models in monolithic state spaces often requires modeling complex distributions that are difficult to represent with either parametric or sample-based approaches. We show that if redundant representations are available, the individual state estimates may be improved by combining simpler dynamical systems, each of which captures some aspect of the complex behavior. For example, human body parts may be robustly tracked individually, but the resulting pose combinations may not satisfy articulation constraints. Conversely, the results produced by full-body trackers satisfy such constraints, but such trackers are usually fragile due to the presence of clutter. We combine constituent dynamical systems in a manner similar to a Product of HMMs model. Hidden variables are introduced to represent system appearance. While the resulting model contains loops, making the inference hard in general, we present an approximate non-loopy filtering algorithm based on sequential application of Belief Propagation to acyclic subgraphs of the model. © Springer-Verlag 2004.