Learning Global and Local Features for License Plate Detection

Publisher:
Springer-Verlag
Publication Type:
Conference Proceeding
Citation:
Neural Information Processing, 2011, pp. 547 - 556
Issue Date:
2011-01
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This paper proposes an intelligent system that is capable of automiatically detecting license plates from static images captured by a digital still camera. A supervised learning apporach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is contructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning apporach and the slelected dSIFT features will be used to construct a stong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing work and promising results are obtained.
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