Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring

碩士 === 逢甲大學 === 資訊工程學系 === 105 === In recent years, the high population density and convenience of vehicles cause the demand of the parking lot or roadside parking increased. When the demand is not available, people usually park the car at will. This sometimes causes the troubles and accidents to ot...

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Main Authors: LIN, LI-WEI, 林立偉
Other Authors: CHANG,KUEI-CHUNG
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/55244325327863121243
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spelling ndltd-TW-105FCU003920392017-09-03T04:26:13Z http://ndltd.ncl.edu.tw/handle/55244325327863121243 Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring 應用於臨時監控之 嵌入式即時車牌辨識系統設計 LIN, LI-WEI 林立偉 碩士 逢甲大學 資訊工程學系 105 In recent years, the high population density and convenience of vehicles cause the demand of the parking lot or roadside parking increased. When the demand is not available, people usually park the car at will. This sometimes causes the troubles and accidents to other people because of shrinking the roadway or blocking the view of corners. For another case, someone may drive to sparsely populated areas and throw a lot of garbage, which will cause the environment pollution. So, the official organization have to configure temporary cameras to make them be punished . In the thesis, we design a real-time and low-cost License Plate Recognition system on embedded platform with general camera, which can be deployed at roadside for temporary monitoring. The record of the recognized license plate number and the corresponding pictures and videos can be sent back to the server for further process. The proposed methods will first set the appropriate environmental parameters. Then, the character contour’s feature is used to find the license plate in the close distance and vertical edges are used to find it in the median distance. Next, perspective transform can convert the plate to face angle, and then unnecessary noises can be filtered. And characters can be separated according to its binary and contour value constraints. Finally, the old and new license plate characters can be identified by the prepared training data with KNN classifier. In the experimental results, the overall recognition accuracy of the license plate number is 86.83%. The accuracy of finding out the license plate is 98.33%. The accuracy of separating characters is 96.69%. The accuracy of character or number recognition is 91.33%. CHANG,KUEI-CHUNG 張貴忠 2017 學位論文 ; thesis 46 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 逢甲大學 === 資訊工程學系 === 105 === In recent years, the high population density and convenience of vehicles cause the demand of the parking lot or roadside parking increased. When the demand is not available, people usually park the car at will. This sometimes causes the troubles and accidents to other people because of shrinking the roadway or blocking the view of corners. For another case, someone may drive to sparsely populated areas and throw a lot of garbage, which will cause the environment pollution. So, the official organization have to configure temporary cameras to make them be punished . In the thesis, we design a real-time and low-cost License Plate Recognition system on embedded platform with general camera, which can be deployed at roadside for temporary monitoring. The record of the recognized license plate number and the corresponding pictures and videos can be sent back to the server for further process. The proposed methods will first set the appropriate environmental parameters. Then, the character contour’s feature is used to find the license plate in the close distance and vertical edges are used to find it in the median distance. Next, perspective transform can convert the plate to face angle, and then unnecessary noises can be filtered. And characters can be separated according to its binary and contour value constraints. Finally, the old and new license plate characters can be identified by the prepared training data with KNN classifier. In the experimental results, the overall recognition accuracy of the license plate number is 86.83%. The accuracy of finding out the license plate is 98.33%. The accuracy of separating characters is 96.69%. The accuracy of character or number recognition is 91.33%.
author2 CHANG,KUEI-CHUNG
author_facet CHANG,KUEI-CHUNG
LIN, LI-WEI
林立偉
author LIN, LI-WEI
林立偉
spellingShingle LIN, LI-WEI
林立偉
Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring
author_sort LIN, LI-WEI
title Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring
title_short Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring
title_full Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring
title_fullStr Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring
title_full_unstemmed Design of Real-Time License Plate Recognition System Based on Embedded Platform for Temporary Monitoring
title_sort design of real-time license plate recognition system based on embedded platform for temporary monitoring
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/55244325327863121243
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