Hand and face tracking under occlusion with anthropomorphic constraints

We propose a graphical model for a decentralized, simultaneous detection and tracking algorithm for efficient localization of hands from a sequence in color and range images. We deduce the location of key-points using a Bayesian framework. We use anthropomorphic constraints for modelling body part articulation. Furthermore, our algorithm reasons about occlusion and preserves data association to deal with ambiguities. Experimental results demonstrate that our system tracks face and hands more accurately in video, compared to prior research.