Modeling and implementing two-stage AdaBoost for real-time vehicle license plate detection.

Song, Moon Kyou; Sarker, Md. Mostafa Kamal

Abstract

License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical detection of car LPs via image processing techniques based on classifier or machine learning algorithms. In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features from LP images. The AdaBoost algorithm is used to classify parts of an image within a search window by a trained strong classifier as either LP or non-LP. Adaptive thresholding is used for the image preprocessing method applied to those images that are of insufficient quality for LPD. This method is of a faster speed and higher accuracy than most of the existing methods used in LPD. Experimental results demonstrate that the average LPD rate is 98.38% and the computational time is approximately 49 ms.

Citation

SONG, M.K. and SARKER, M.M.K. 2014. Modeling and implementing two-stage AdaBoost for real-time vehicle license plate detection. Journal of applied mathematics [online], 2014: advanced mathematics and numerical modeling of IoT (Internet of Things), article ID 697358. Available from: https://doi.org/10.1155/2014/697658

• Journal Article Type: Article
• Acceptance Date: Jul 30, 2014
• Online Publication Date: Aug 14, 2014
• Publication Date: Dec 31, 2014
• Deposit Date: Dec 4, 2021
• Publicly Available Date: Aug 25, 2022
• Journal: Journal of Applied Mathematics
• Print ISSN: 1110-757X
• Electronic ISSN: 1687-0042
• Publisher: Hindawi
• Peer Reviewed: Peer Reviewed
• Volume: 2014
• Article Number: 697658
• DOI: https://doi.org/10.1155/2014/697658
• Keywords: License plate (LP) detection; LP recognition system; Machine learning algorithms; Adaptive boosting (AdaBoost)
• Public URL: https://rgu-repository.worktribe.com/output/1542202