動態車牌辨識系統之研究

莊祥琳

doi:10.6846/TKU.2007.00093

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動態車牌辨識系統之研究

A Study of Motion Based Vehicle License Plates Recognition System

莊祥琳 , Masters　　Advisor：謝景棠　　

繁體中文 DOI： 10.6846/TKU.2007.00093

車牌辨識；小波理論；型態學；類神經網路； Wavelet transform ； Morphology ； License Plate Recognition ； Neural Network

Abstract │ Reference (22) │ Times Cited (3) │ Altmetrics

Abstract 〈TOP〉

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License plate recognition system has been extensively used in a variety of applications, such as parking lot management of community and buildings, highway toll systems, car management and so on. Most commonly available license plate recognition systems recognize only in static pictures. In this study, a motion-based targets vehicle license plate recognition prototype is proposed. Multiple-frames of pictures are used for recognition. As a result, the time for preprocessing stages is critical and developing time-efficient algorithms become more important. This prototype is designed mainly for specific car-search that should be helpful for many organizations such as police departments and bank.
    This paper presents a algorithm for license plate recognition using Discrete Wavelet Transform (DWT) and Neural Network. Classified into four parts: Locating the car-back in an image. Locating the license plate. Segmenting characters of the license plate. Recognizing each character of the license plate.
    We accomplish the license-plate localization by high-pass wavelet coefficients. Since the amount of data becomes 1/4 only, we can reduce, a lot of the system required time, the computational complexity, the memory usage, and rid of noise. This methodology provides high efficiency for locating a license plate from an image.
    In the recognition, we use the neural network because of the ability that a large number of neurons imitate the neural network of living beings. Besides, neural network has high and getting fault-tolerant, and that is helpful to against the noisy or incomplete cut characters.
In this system, we set video camera under the rear-view mirror and continuously shoot the front motion-based vehicle. The video can be taken under arbitrary weathers and illuminations. In order to test the system efficiency, there are 257 cars been tested.

Reference ( 22 ) 〈TOP〉

[1] T. Naito, et al., “Robust License-Plate Recognition Method for Passing Vehicles under outside Environment,” IEEE Trans. on Vehicular Technology, vol. 49, no. 6, Nov. 2000, pp. 2309-2319.
連結：
[2] Y. Freund, “An Adaptive Version of the Boost by Majority Algorithm,” Machine Learning, 43(3), 2001, pp. 293-318.
連結：
[3] W. G. Zhu, G. J. Hou, X. Jia; “A study of locating vehicle license plate based on color feature and mathematical morphology.” Signal Processing, 2002 6th International Conference , Aug. 2002 748-751.
連結：
[4] Z. B. Huang, and Y. F. Guo, “Classifier Fusion-Based Vehicle License Plate Detection Algorithm,“ in IEEE International Conference on machine Learning and Cybernetics, vol. 5, Nov. 2003, pp. 2984-2989.
連結：
[6] T. D. Duan, D. A. Duc, T. L. Hong Du.”Combining Hough Transform and Contour Algorithm for detecting Vehicles’ License-Plates” Digital Object Identifier Conference. 2004, Page(s):747-750.
連結：

Times Cited (3) 〈TOP〉

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