Resolving visual uncertainty and occlusion through probabilistic reasoning

Tracking interacting human body parts from a single two-dimensional view is difficult due to occlusion, ambiguity and spatio-temporal discontinuities. We present a Bayesian network method for this task. The method is not reliant upon spatio-temporal continuity, but exploits it when present. Our inference-based tracking model is compared with a CONDENSATION model aug-mented with a probabilistic exclusion mechanism. We show that the Bayesian network has the advantages of fully modelling the state space, explicitly rep-resenting domain knowledge, and handling complex interactions between variables in a globally consistent and computationally effective manner.