A Robust Framework for Vehicle Detection and Tracking Based on Particle Filter

DOI

10.2991/jimet-15.2015.150How to use a DOI?

Keywords

Vehicle detection/tracking; Foreground extraction; Particle filter; Histogram of oriented gradients; HSV color space; Occlusion segmentation

Abstract

Vehicle detection and tracking have been promising application in traffic surveillance and vehicular network. However, the vision-based approach still remains a challenging task due to the problems of illumination variation, shadow and occlusion. In this paper, we propose a robust framework mainly concatenates on two aspects: adaptive vehicle detection with shadow removal, and vehicle tracking with occlusion handling. Firstly, for vehicle detection stage, an improved ViBe algorithm with ghost suppression is adopted to extract moving vehicle region. Then moving shadow is removed by integrating improved color with texture feature. Then, aiming to achieve the multi-vehicle tracking, we propose an enhanced histogram of the oriented gradient combined with HSV color space based on particle filter (ECHOGPF). Finally, we employ the occlusion detection and occlusion segmentation to refine our system, which are based on one-dimensional maximum entropy and the least square ellipse fitting. Experiments on popular datasets show that our proposed system has a good effectiveness, e.g., the accuracy is 95% on the vehicle tracking.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huihui Liu
AU  - Yong Liu
AU  - Haiqing Du
PY  - 2015/12
DA  - 2015/12
TI  - A Robust Framework for Vehicle Detection and Tracking Based on Particle Filter
BT  - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
PB  - Atlantis Press
SP  - 803
EP  - 811
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimet-15.2015.150
DO  - 10.2991/jimet-15.2015.150
ID  - Liu2015/12
ER  -