Learning-based License Plate Detection on Edge Features

This paper presents Adaboost learning-based method for license plate detection in unconstrained environment (cluttered scenes, changing illumination, in-plane and out-plane rotation of license plates). Our approach is motivated by the idea that learning-based method can implicitly derive a robust object model through training using large set of positive and negative samples. In addition, edge rather than intensity information is used to train license plate detector (LPD) since edge information – using canny edge detector – has shown better representation than intensity for license plate problem. We present comparative results of our approach against intensity, selection of different number of stages as well as our LPD detection speed. Our approach achieves true positive rate of ~70%, with detection speed ~80 ms for image size of

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