Rapid license plate detection using Modest AdaBoost and template matching

Kam Tong Sam; Xiao Lin Tian

doi:10.1117/12.853423

26 February 2010 Rapid license plate detection using Modest AdaBoost and template matching

Kam Tong Sam, Xiao Lin Tian

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75460W (2010) https://doi.org/10.1117/12.853423
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

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.

Citation Download Citation

Kam Tong Sam and Xiao Lin Tian "Rapid license plate detection using Modest AdaBoost and template matching", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460W (26 February 2010); https://doi.org/10.1117/12.853423

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