People tracking with body pose estimation for human path prediction

Abstract

Prediction and observation of human motion are essential functions for robots co-existing with humans in everyday environments. We propose a people motion tracking and prediction approach by using the advantage of detailed 3D information about the positions of body joints. Using the shoulder position displayed in a geometrical skeleton diagram of a human's upper body part, the body pose from the proposed human kinematic model is estimated. Human motion tracking and path prediction are achieved via the extended Kalman Filter. The proposed method is verified in an indoor environment where humans pass by each other. Experiment results demonstrate that walking people and their body pose are robustly tracked and predicted accurately, with less occlusions compared to traditional human tracking.