GPU Accelerated Number Plate Localization in Crowded Situation

(1) Adhi Prahara Mail (Universitas Ahmad Dahlan, Indonesia)
(2) * Andri Pranolo Mail (Universitas Ahmad Dahlan, Indonesia)
(3) Rafał Dreżewski Mail (AGH University of Science and Technology, Poland)
*corresponding author


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 stills persist especially in crowded situation where many vehicles present. Therefore, a method is proposed to localize number plate in crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate then detect 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 test result shows good results, 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 50 ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plate


Number Plate; Plate Localization; Vertical Edge Detection; HOG; SVM; GPU



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International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan,  UTM Big Data Centre - Universiti Teknologi Malaysia, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0