動態多標的車牌辨識系統之研究

魏銪志

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動態多標的車牌辨識系統之研究

Motion Based Multiple Vehicle License Plate Recognition

魏銪志 , Masters　　Advisor：李錫捷　　

繁體中文

汽車牌照自動辨識；樣型識別；光學字元閱讀；類神經網路；影像處理； Vehicle License Plate Recognition ； Pattern Recognition ； Neural Networks ； Vehicle Identification Number ； Computer Vision ； SimNet

Abstract │ Reference (31) │ Times Cited (15) │ Altmetrics

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License plate recognition systems may be deployed in a variety of applications such as, highway toll systems, traffic control systems, or parking lot management systems. Due to the volatile work environments, it is not practical for a system to claim 100% recognition rate. However, under the circumstances where the environment is controlled to a certain degree, it is not uncommon for the vehicle license plate recognition rate to reach the level of 90s.
           Most commonly available license plate recognition systems recognize only static pictures. In this study, a motion-based multiple 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 or highway patrol departments.
           In this study, a real time hybrid approach is proposed for motion-based multiple targets vehicle license plate recognition.  A system prototype will be built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle.  The prototype consists of four major modules, including the module for image processing, the module for license plate region identification, the module for character extraction, and the module for pattern recognition called SimNet.

Reference ( 31 ) 〈TOP〉

[6] M. J. Dalgeish, “Vehicle Detection for Advanced Transport Telematics”, Road Traffic Monitoring and Control, 1994, pp.164-167.
連結：
[13] 仲崇實, 「影像處理與類神經乏晰方法於車輛牌照自動辨識之應用研究」, 私立元智大學資訊研究所, 碩士論文, 1998.
連結：
[15] S. Bohrer, T. Zielke, V. Freiburg, “An Integrated Obstacle Detection Framework for Intelligent Cruise Control on Motorways”, Intelligent Vehicles ''95 Symposium, 1995, pp. 276-281.
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[16] M. Betke, E. Haritaoglu, L. S. Davis, “Multiple Vehicle Detection and Tracking in Hard Real-Time”, Intelligent Vehicle Symposium, 1996, pp. 351-356.
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[18] N. Otsu, “A Threshold Selection Method from Gray-Level Historgams”, IEEE Transactions on System,Man,and Cybernetics, vol. SMC-9, 1979, pp. 62-66.
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