Physics-based modelling of human motion using Kalman filter and collision avoidance algorithm

The paper deals with the problem of computer vision based multi-person motion tracking, which in many cases suffers from lack of discriminating features of observed persons. To solve this problem, a physics based model of human motion is proposed, which includes internal forces of the persons by the means of the Kalman filter, and the cylindrical envelopes, which produce collision avoiding forces when observed persons 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.