Online tracking and mimicking of human movements by a humanoid robot

Abstract

This paper describes a humanoid robot system that can capture and mimic the motion of human body parts in real-time. The underlying vision system is able to automatically detect and track human body parts such as hands and faces in images captured by the robot's eyes. It is based on a probabilistic approach that can detect and track multiple blobs in a 60-Hz stereo image stream on a standard dual processor PC. A random jerk model is employed to approximate the observed human motion and a Kalman filter is used to estimate its parameters (three-dimensional positions, velocities and accelerations). This enables the system to realistically mimic the perceived motion in real-time.