| 二次定位车牌分割及识别方法 |
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| 引用本文： | 刘尚旺,段德全,崔艳萌,周猛.二次定位车牌分割及识别方法[J].河南师范大学学报(自然科学版),2016(4):151-156. |
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| 作者姓名： | 刘尚旺 段德全 崔艳萌 周猛 |
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| 作者单位： | 河南师范大学计算机与信息工程学院;河南师范大学"智慧商务与物联网技术"河南省工程实验室 |
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| 基金项目： | 国家自然科学基金(U1304607);河南省高等学校重点科研项目(15A520080);河南师范大学博士科研启动基金(qd12138) |
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| 摘 要： | 智慧城市建设中,需对重点街道和路口采集到的交通视频文件进行智能分析.为此,提出一种二次定位车牌分割、识别方法.首先,利用垂直投影区域特征并结合Hough变换得到车牌的粗略定位分割结果;然后,基于该车牌图像的粗略定位分割结果,采用支持向量机的方法,进行车牌的精细定位分割并对车牌号码进行自动提取、识别.通过对多源车流量视频实验数据中的1680帧车牌图像进行自动车牌提取分析,在5°和10°两个倾斜角度,二次定位车牌识别方法的准确率分别达到96.7%和96.2%,优于相关算法.
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| 关 键 词： | 车牌识别 智慧城市 视频文件 垂直投影区域特征 支持向量机 |
Vehicle License Plate Segmentation and Recognition Method Based on Two Times Locations |
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| LIU Shangwang;DUAN Dequan;CUI Yanmeng;ZHOU Meng.Vehicle License Plate Segmentation and Recognition Method Based on Two Times Locations[J].Journal of Henan Normal University(Natural Science),2016(4):151-156. |
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| Authors: | LIU Shangwang;DUAN Dequan;CUI Yanmeng;ZHOU Meng |
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| Institution: | LIU Shangwang;DUAN Dequan;CUI Yanmeng;ZHOU Meng;College of Computer and Information Engineering,Henan Normal University;Engineering Lab of Intelligence Business &Internet of Things,Henan Normal University; |
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| Abstract: | During the construction of smart city,it is necessary to analyze the traffic video files collected in the main streets and crossroads.Therefore,a vehicle license plate segmentation and recognition method based on two times locations was proposed in this paper.Firstly,the coarse segmentation result of license location was obtained based on the vertical projection regional characteristic and Hough transform.Secondly,the support vector machine(SVM)was adopted to locate,segment and recognize the vehicle license plate number automatically and accurately.After analyzing the 1680 license plate images in multisource traffic video,the accuracy of the proposed method can reach 96.7% and 96.2%in the two angles of 5and 10 degrees,which is better than considered algorithms. |
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| Keywords: | vehicle license plate recognition smart city video files vertical projection regional characteristic support vector machine(SVM) |
| 本文献已被 CNKI 等数据库收录！ |
