2D and 3D upper body tracking with one framework
We propose a Dynamic Bayesian Network (DBN) model for upper body tracking. We first construct a Bayesian Network (BN) to represent the human upper body structure and then incorporate into the BN various generic physical and anatomical constraints on the parts of the upper body. Unlike the existing upper body models, ours aims at handling physically feasible body motion rather than only some typical motion patterns. We also explicitly model part self-occlusion in the DBN model, which allows to automatically detect the occurrence of self-occlusion and to minimize the effect of measurement errors on the tracking accuracy due to occlusion. Moreover, our method can handle both 2D and 3D upper body tracking within the same framework. Using the DBN model, upper body tracking can be achieved through probabilistic inference over time.