Real-Time Vehicle License Plate Detection Based on Background Subtraction and Cascade of Boosted Classifiers

Title & Authors
Real-Time Vehicle License Plate Detection Based on Background Subtraction and Cascade of Boosted Classifiers
Sarker, Md. Mostafa Kamal; Song, Moon Kyou;

Abstract
License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection framework which combines with an adaptive GMM (Gaussian Mixture Model) and a cascade of boosted classifiers to make a faster vehicle LP detector. To develop a background model by using a GMM is possible in the circumstance of a fixed camera and extracts the motions using background subtraction. Firstly, an adaptive GMM is used to find the region of interest (ROI) on which motion detectors are running to detect the vehicle area as blobs ROIs. Secondly, a cascade of boosted classifiers is executed on the blobs ROIs to detect a LP. The experimental results on our test video with the resolution of $\small{720{\times}576}$ show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms.
Keywords
Language
English
Cited by
1.
Segmentation and Recognition of Korean Vehicle License Plate Characters Based on the Global Threshold Method and the Cross-Correlation Matching Algorithm,;;

Journal of Information Processing Systems, 2016. vol.12. 4, pp.661-680
1.
Segmentation and Recognition of Korean Vehicle License Plate Characters Based on the Global Threshold Method and the Cross-Correlation Matching Algorithm, Journal of Information Processing Systems, 2016, 2092805X
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