The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System

Sammanfattning: In this thesis, the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter using a pair of stereo vision system to overcome label discontinuity and robust tracking in an Intelligent Vision Agent System (IVAS) is evaluated. This filter is widely used in multiple-target tracking applications such as surveillance, human tracking, radar, and etc. A pair of cameras is used to get the left and right image sequences in order to extract 3-D coordinates of targets’ positions in the real world scene. The 3-D trajectories of targets are tracked by GM-PHD filter. Many tracking algorithms fail to simultaneously maintain stability of tracking and label continuity of targets, when one or more targets are hidden for a while to camera’s view. The GM-PHD filter performs well in tracking multiple targets; however, the label continuity is not maintained satisfactorily in some situations such as full occlusion and crossing targets. In this project, the label continuity of targets is guaranteed by a new method of labeling, and the simulation results show satisfactory results. A random walk motion is used to validate the ability of the algorithm in tracking and maintaining targets’ labels. In order to evaluate the performance of the GM-PHD filter, a 3-D spatial test motion model is introduced. Here, the two target trajectories are generated in a way that either occlusion or crossing occurs in some time intervals. Then, the two key parameters, angular velocity and motion speed, are used to evaluate the performance of algorithm. The simulation results for two moving targets in occlusion and crossing show that the proposed system not only robustly tracks them, but also maintains the label continuity of two targets.