Physics-Based Modelling of Human Motion using Kalman Filter and Collision Avoidance Algorithm

The paper deals with the problem of computer vision based multi-Peršon motion tracking, which in many cases suffers from lack of discriminating features of observed Peršons. To solve this problem, a physics based model of human motion is proposed, which includes intertial forces of the Peršons by the means of the Kalman filter, and the cylindrical envelopes, which produce collision avoiding forces when observed Peršons come to close proximity. We tested the proposed method on two sequences, one from squash match, and the other from the basketball play and found out that the number of tracker mistakes significantly decreased.