The Evaluation of Gaussian Mixture Approximation of the Probability Hypothesis Density Approach for Multi-Object Tracking
No Thumbnail Available
Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Abstract
This paper describes the performance of the Gaussian Mixture Probability Hypothesis Density (GMPHD)
filter for multiple human tracking in an intelligent vision system. Human movement trajectories were
observed with a camera and tracked by the GM-PHD filter. The filter multi-target tracking ability was validated
by two random motion trajectories in the paper. To evaluate the filter performance in relation to the target
movement, the motion velocity and angular velocity as key evaluation factors were proposed. A circular motion model was implemented for simplified analysis of the filter tracking performance. The results indicate that the mean absolute error defined as the difference between the filter prediction and the ground truth is proportional to the motion speed and angular velocity of the target. The error is only slightly affected by the tracking targets’ number.
Description
Keywords
Human Tracking, Probability Hypothesis Density, Performance Evaluation, Vision System