Entropy distribution and coverage rate-based birth intensity estimation in GM-PHD filter for multi-target visual tracking

Abstract

Tracking multiple moving targets in video is a challenge because of the presence of noisy video data and varying numbers of targets, and data association problems. In this paper, a multi-target visual tracking system that combines object detection with the Gaussian mixture probability hypothesis density filter is developed, in which a new birth intensity estimation method based on entropy distribution and coverage rate is proposed. The birth intensity is first initialized by the previously obtained target states and measurements. The measurements are obtained by object detection and are classified into the birth measurements and the survival measurements. The currently obtained birth measurements are then used to update the birth intensity. In the update stage, the entropy distribution is incorporated to remove some noises within the initialized birth intensity that are irrelevant to the birth measurements. The coverage rate between each birth intensity component and the corresponding birth measurement is computed to further eliminate the noises. Experiments on noisy video sequences are conducted to show the good performance of the proposed visual tracking system.