The Evaluation of the Gaussian Mixture Probability Hypothesis Density Approach for Multi-target Tracking

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Jiandan Chen, Oyekanlu Emmanuel Adebomi, Onidare Samuel Olusayo, Wlodek Kulesza
Title: The Evaluation of the Gaussian Mixture Probability Hypothesis Density Approach for Multi-target Tracking
Conference name: IEEE International Conference on Imaging Systems and Techniques
Year: 2010
Publisher: IEEE
City: Thessaloniki
URI/DOI: 10.1109/IST.2010.5548541
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Electrical Engineering (Sektionen för ingenjörsvetenskap - Avd. för elektroteknik)
School of Engineering S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/ing/
Authors e-mail: jdc@bth.se, wka@bth.se
Language: English
Abstract: This paper describes the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) 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.
Subject: Signal Processing\Filter Design
Signal Processing\General
Keywords: Human Tracking, Probability Hypothesis Density, Performance Evaluation, Vision System
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