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Proceedings Paper

Rapid license plate detection using Modest AdaBoost and template matching
Author(s): Kam Tong Sam; Xiao Lin Tian
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Paper Abstract

License plate detection and recognition are vital yet challenging tasks for law enforcement agencies. This paper presents a license plate detection prototype system for a Macao law enforcement department using Modest Adaboost combined with template matching technique. Firstly, a machine learning algorithm, based on Modest AdaBoost which mostly aims for better generalization capability and resistance to overfitting, was applied to find out candidate license plates over the input images. In the second stage, template matching technique was employed to verify the license plate appearances in order to reduce false positives. This paper shows that the AdaBoost algorithm, which was originally used for face detection, has successfully been applied to solve the problems of license plate detection. Experimental results demonstrate high accuracy and efficiency of the proposed method.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460W (26 February 2010); doi: 10.1117/12.853423
Show Author Affiliations
Kam Tong Sam, Macau Univ. of Science and Technology (Macao, China)
Xiao Lin Tian, Macau Univ. of Science and Technology (Macao, China)

Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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