A model-based humanoid perception system for real-time human motion imitation

This paper presents real-time human motion analysis based on hierarchical tracking and inverse kinematics. Our goal is to implement a mechanism of human-machine interaction that permits a robot to learn from human gestures, and, as a first stage, we have developed a computer-vision based human upperbody motion analysis system. This application requires developing a real-time human motion capturing system that works without special devices or markers. Since such a system is unstable and can only acquire, partial information because of self-occlusions, we have introduced a pose estimation method based on inverse kinematics. This system can estimate upper-body human postures with limited perceptual cues such as position of head and hands. The method has been tested using a HOAP-I humanoid robot.