Human Motion Parameter Capturing Using Particle Filter and Nonparametric Belief Propagation

We propose a motion capturing system for human walking in the side view. We build a 3D human model with structural and kinematical constraints and then use the Particle Filter (PF) and Nonparametric Belief Propagation (NBP) for human tracking. To reduce the high-dimensional parameters, the separated particle filter for tracking six parts of human body is used. PF will estimate some initial pose, and then NBP will compute the results after several iterations. In the experiments, we show the estimated motion parameter of each frame. The error angle of our system is less than 11 degrees.