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


Vol. 39,  No. 10, pp. 909-919, Oct.  2014


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  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 720x576 show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms.

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  Cite this article

[IEEE Style]

M. M. K. Sarker and M. K. Song, "Real-Time Vehicle License Plate Detection Based on Background Subtraction and Cascade of Boosted Classifiers," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 10, pp. 909-919, 2014. DOI: .

[ACM Style]

Md. Mostafa Kamal Sarker and Moon Kyou Song. 2014. Real-Time Vehicle License Plate Detection Based on Background Subtraction and Cascade of Boosted Classifiers. The Journal of Korean Institute of Communications and Information Sciences, 39, 10, (2014), 909-919. DOI: .

[KICS Style]

Md. Mostafa Kamal Sarker and Moon Kyou Song, "Real-Time Vehicle License Plate Detection Based on Background Subtraction and Cascade of Boosted Classifiers," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 10, pp. 909-919, 10. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880