Anti-Alteration Technology for License Plate Recognition System

Abstract:

Article Preview

In order to realize anti-alteration function for License Plate Recognition System (LPRS), a uniform-field imaging system is designed and a corresponding anti-alteration algorithm is proposed. First, reflection characteristics of license plate and typical alteration material are measured. As a result, the two characteristics in near-infrared range fluctuate moderately and the former is notably lower than the latter. Then the uniform-field imaging system for visible-light and near-infrared is designed to capture the difference above effectively. Finally, the anti-alteration algorithm, composed of license plate location, character matching segmentation and alteration recognition, is introduced. Experimental results have indicated that visible-light and near-infrared images can be acquired stably by the proposed system under the condition of natural illumination and there are discriminable gray differences between license plate and alteration material in near-infrared images; and that success rate and average executive time of the algorithm are 86.5% and 157ms respectively.

Info:

Periodical:

Advanced Materials Research (Volumes 211-212)

Edited by:

Ran Chen

Pages:

156-160

DOI:

10.4028/www.scientific.net/AMR.211-212.156

Citation:

X. Yang et al., "Anti-Alteration Technology for License Plate Recognition System", Advanced Materials Research, Vols. 211-212, pp. 156-160, 2011

Online since:

February 2011

Export:

Price:

$35.00

[1] X. J. Wang: Review of the Process and Dynamics of ITS Research and Developments. Urban Transport of China, Vol. 6, No. 1 (2008), p.6.

[2] C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psporoulas, et al.: A License Plate-Recognition Algorithm for Intelligent Transportation System Applications. IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No. 3 (2006).

DOI: 10.1109/tits.2006.880641

[3] C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, et al.: License Plate Recognition From Still Images and Video Sequences: A Survey. IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 3 (2008), p.377.

DOI: 10.1109/tits.2008.922938

[4] S. M. Youssef, and S. B. Abdelrahaman: A smart access control using an efficient license plate location and recognition approach. Expert Systems with Applications, Vol. 34 (2008), p.256.

DOI: 10.1016/j.eswa.2006.09.013

[5] H. Cancer, H. S. Gecim, A. Z. Alkar, et al.: Efficient Embedded Neural-Network-Based License Plate Recognition System. IEEE Transactions on Vehicular Technology, Vol. 57, No. 5 (2008), p.2675.

DOI: 10.1109/tvt.2008.915524

[6] T. Naito, T. Tsukada, K. Yamada, et al.: Robust License-Plate Recognition Method for Passing Vehicles Under Outside Environment. IEEE Transactions on Vehicular Technology, Vol. 46, No. 6 (2000), p.2309.

DOI: 10.1109/25.901900

[7] J. B. Jiao, Q. X. Ye and Q. M. Huang: A configurable method for multi-style license plate recognition. Pattern Recognition, Vol. 42 (2009), p.358.

DOI: 10.1016/j.patcog.2008.08.016

[8] X. Fan and G. L. Fan: Graphical Models for Joint Segmentation and Recognition of License Plate Characters. IEEE Signal Processing Letters, Vol. 16, No. 1 (2009), p.10.

DOI: 10.1109/lsp.2008.2008486

[9] V. Abolghasemi and A. Ahmadyfard: An edge-based color-aided method for license plate detection. Image and Vision Computing, Vol. 27 (2009), p.1134.

DOI: 10.1016/j.imavis.2008.10.012

[10] J. I. Yamaguchi, T. Kita, and Y. Ishihara: Detection of License Plate Using Matched Filter. Electronics and Communications in Japan, Vol. 91, No. 9 (2008), p.20.

DOI: 10.1002/ecj.10004

[11] D. J. Kang: Dynamic Programming-based Method for Extraction of License Plate Numbers of Speeding Vehicles on The Highway. International Journal of Automotive Technology, Vol. 10, No. 2 (2009), p.205.

DOI: 10.1007/s12239-009-0024-2

[12] J. M. Guo and Y. F. Liu: License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques. IEEE Transactions on Vehicular Technology, Vol. 57, No. 3 (2008), p.1417.

DOI: 10.1109/tvt.2007.909284

[13] W. J. Jia, H. F. Zhang and X. J. He: Region-based license plate detection. Journal of Network and Computer Applications, Vol. 30 (2007), p.1324.

[14] D. N. Zhang, Y. N. Zhao and J. X. Wang: An efficient method of license plate location. Pattern Recognition Letters, Vol. 26 (2005), p.2431.

DOI: 10.1016/j.patrec.2005.04.014

[15] N. Otsu. A threshold selection method from grey-level histogram: IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, No. 1 (1979), p.62.

[16] J Kittler and J Illingworth: On threshold selection using clustering criteria. IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, No. 5 (1985), p.652.

DOI: 10.1109/tsmc.1985.6313443

In order to see related information, you need to Login.