Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach

Abstract: Motion tracking is an important part of the Intelligent Vision Agent System, IVAS. In this thesis, the Gaussian mixture approximation of the Probability Hypothesis Density filter (GM-PHD) was implemented to provide a reliable and computationally efficient multiple human tracker in the activity space of the IVAS. The GM-PHD filter estimates both the number and states of multiple targets by propagating the first order moment of the posterior distribution of the targets state space. A typical room dimension was adopted as the activity space. Human movements were simulated to show the position of human(s) at different instants in the activity space. The human movement path(s) or trajectory(ies) were observed with a camera and tracked with the GM-PHD filter. The implementation of the GM-PHD filter algorithm in tracking multiple target(s) across the activity space was validated using the random free walk motion type. The mean error in the filter prediction and ground truth was measured against velocity and angular alteration of a circular motion model using the Wasserstein’s error distance. The result of this work shows that the GM-PHD filter is reliable for multiple target tracking.