This paper presents a method for markerless human motion capture using a single camera. It uses tree-based filtering to efficiently propagate a probability distribution over poses of a 3D body model. The pose vectors and associated shapes are arranged in a tree, which is constructed by hierarchical pairwise clustering, in order to efficiently evaluate the likelihood in each frame. A new likelihood function based on silhouette matching is proposed that improves the pose estimation of thinner body parts, i.e. the limbs. The dynamic model takes self-occlusion into account by increasing the variance of occluded body-parts, thus allowing for recovery when the body part reappears. We present two applications of our method that work in real-time on a Cell Broadband EngineTM: a computer game and a virtual clothing application.

This paper describes methods of tracking of moving objects in a cluttered background by integrating optical flow, depth data, and/or uniform brightness regions. First, a basic method is introduced which extracts a region with uniform optical flow as the target region. Then an extended method is described in which optical flow and depth are fused. A target region is extracted by Baysian inference in term of optical flow, depth and the predicted target location. This method works only for textured objects because optical flow or depth are extracted for textured objects. In order to solve this problem, uniform regions in addition to the optical flow are used for tracking. Realtime human tracking is realized for real image sequences by using a real time processor with multiple DSPs.