Moving Vehicles Detection Based on Improved Gaussian Mixture Model

DOI

10.2991/eame-15.2015.210How to use a DOI?

Keywords

transportation safety; vehicles detection; Gaussian mixture model (GMM); frame difference

Abstract

Vehicles detection has a great significance on traffic congestion relief, traffic accidents prevention and treatment. This paper proposes an improved Gaussian mixture model (GMM) to detect moving vehicles because GMM always detects background for foreground in complex environment. The new algorithm combines GMM with frame difference. Then, the whole vehicle can be detected by this new algorithm because of the enhanced demarcation point between background and foreground. Finally, the moving vehicles detection system is established. Experiments in different weather (sunny and rainy) and different time (day and night) have been made. They can prove that the new algorithm has been greatly improved in the aspects of adaptability, accuracy, real-time and so on. The moving vehicles can also be detected correctly and effectively in the situation with various complicated factors.

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  - Y.L. Ma
AU  - Y.C. Li
AU  - Z.C. Zhang
PY  - 2015/07
DA  - 2015/07
TI  - Moving Vehicles Detection Based on Improved Gaussian Mixture Model
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 778
EP  - 781
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.210
DO  - 10.2991/eame-15.2015.210
ID  - Ma2015/07
ER  -