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Proceedings Paper

Robust and fast license plate detection based on the fusion of color and edge feature
Author(s): De Cai; Zhonghan Shi; Jin Liu; Chuanping Hu; Lin Mei; Li Qi
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Paper Abstract

Extracting a license plate is an important stage in automatic vehicle identification. The degradation of images and the computation intense make this task difficult. In this paper, a robust and fast license plate detection based on the fusion of color and edge feature is proposed. Based on the dichromatic reflection model, two new color ratios computed from the RGB color model are introduced and proved to be two color invariants. The global color feature extracted by the new color invariants improves the method’s robustness. The local Sobel edge feature guarantees the method’s accuracy. In the experiment, the detection performance is good. The detection results show that this paper’s method is robust to the illumination, object geometry and the disturbance around the license plates. The method can also detect license plates when the color of the car body is the same as the color of the plates. The processing time for image size of 1000x1000 by pixels is nearly 0.2s. Based on the comparison, the performance of the new ratios is comparable to the common used HSI color model.

Paper Details

Date Published: 24 November 2014
PDF: 8 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93012Z (24 November 2014); doi: 10.1117/12.2073106
Show Author Affiliations
De Cai, Beijing Institute of Technology (China)
Zhonghan Shi, Beijing Institute of Technology (China)
Jin Liu, Beijing Institute of Technology (China)
Chuanping Hu, The Third Research Institute of Ministry of Public Security (China)
Lin Mei, The Third Research Institute of Ministry of Public Security (China)
Li Qi, The Third Research Institute of Ministry of Public Security (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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