GPU accelerated number plate localization in crowded situation

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Abstract

Number Plate Localization (NPL) has been widely used as part of Automatic Number Plate Recognition (ANPR) system. NPL method determines the accuracy of ANPR system. Although it is a mature research, the challenge still persists especially in crowded situation where many vehicles are present. Therefore, a method is proposed to localize number plate in a crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate and then detects the number plate using combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method employs GPU to deal with multiple number plate detection, to handle multi-scale detection window, and to perform real time detection. The experimental results are very promising, 0.9883 value of AUC (Area Under Curve), and 0.9362 of BAC (Balance Accuracy). Moreover, potential real time detection is foreseen because total process is executed in less than 50ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plate.

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APA

Prahara, A., Pranolo, A., & Dreżewski, R. (2015). GPU accelerated number plate localization in crowded situation. International Journal of Advances in Intelligent Informatics, 1(3), 150–157. https://doi.org/10.26555/ijain.v1i3.46

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