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 |
