Efficient 3D Upper Body Tracking with Self-Occlusions

We propose an efficient 3D upper body tracking method, which recovers the positions and orientations of six upper-body parts from the video sequence. Our method is based on a probabilistic graphical model (PGM), which incorporates the spatial relationships among the body parts, and a robust multi-view image likelihood using probabilistic PCA (PPCA). For the efficiency, we use a tree-structured graphical model and use the particle based belief propagation to perform the inference. Since our image likelihood is based on multiple views, we address the self-occlusion by modeling the likelihood of the body part in each view, and automatically decrease the influence of the occluded view in the inference procedure.