Birth intensity online estimation in GM-PHD filter for multi-target visual tracking

摘要

Multi-target tracking in video is a challenge due to noisy video data, varying number of targets, and the data association problems. In this paper, a multi-target visual tracking system that incorporates object detection with the Gaussian mixture PHD filter is developed. The main contribution of this paper is to propose a new birth intensity online estimation method that based on the entropy distribution and the coverage rate. First, the birth intensity is initialized by using the previously obtained targets' states and measurements. The measurements are obtained by object detection and classified into the birth measurements and the survival measurements. Then it is updated according to the currently obtained birth measurements. In the update stage, the instability of the entropy distribution is applied to remove components like noises within the birth intensity which are irrelevant with the currently obtained birth measurements. And the coverage rate between each birth intensity component and corresponding birth measurement is computed to further eliminate the noises. Finally, experiments are implemented to show the performance of the proposed visual tracking system, especially to show the good performance for tracking the newborn targets.