Real-time human motion capturing system

This paper presents a real time vision-based human motion capturing and recognition system using two calibrated CCD cameras. We propose a simple but effective method to estimate the motion parameters (BAPs) of the human object by analyzing the vertical projection profile and the horizontal projection profile in each view to identify different arm and leg postures. With the identified postures, we can apply the Kalman filtering to capture the motion parameters (joint angles). Our method is divided into macro motion analysis and micro motion analysis. The former identifies certain well-defined postures and the latter traces the variation of joint angle or BAPs. In the experiments, we test 22 different arm and leg postures and show the errors of the estimated BAPs.