Research On 3D Human Motion Tracking Based On Probabilistic Model

This thesis focuses on the automatic recovery of three-dimensional human motion from multiple synchronized video sequences. The potential applications of this kind of markerless motion capture technique are motion analysis, medical diagnosis, virtual reality, computer animation, video surveillance, human-computer interface and so on. 3D human motion tracking faces difficulties caused by non-rigid human model representation, 2D-3D projection, self occlusion, high dimensionality of state space and image features extraction under clutter. It is a challenging task in the field of computer vision.This thesis proposes a model based 3D human motion tracking framework, where the articulated human model represented by truncated cones is matched with several image features, such as silhouette, edge, intensity and skin color. And human motion tracking is the problem of state estimation, which can be accomplished by the particle filter algorithm based on probabilistic model.The particle filter algorithm, having the advantage of tracking under clutter and self occlusion, still suffers the pain of high computing complexity during 3D human motion estimation. This thesis proposes two improvements of standard particle filter. Firstly, for the purpose of improving the accuracy of posterior distribution, state space decomposition and PERM (Pruned-Enriched Rosenbluth Method) sampling are adopted during the annealed particle filter. Secondly, a new particle filter based sampling framework, which combines the local optimization and stochastic sampling, is proposed. The most important feature of this sampling method is that the optimization result is used to guide the importance function, which suits for estimation of multi-modal distribution in high dimensionality.Further more, this thesis proposes a novel method based on a non-parametric background model to detect the silhouette of human body in video sequences. This background subtraction method utilizes intensity and edge features synchronously to improve robustness of the foreground detection. And an adaptive shadow detection model is used to find the accurate moving objects.The algorithm is tested on simulative and real video sequences, which include human motion with self-occlusion, and can accomplish the 3D human motion tracking tasks.