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基于省域路网的高速公路联网监控与人脸识别技术改进
引用本文:陈钊正,郭晓峰,谭政宇,张薇.基于省域路网的高速公路联网监控与人脸识别技术改进[J].科学技术与工程,2018,18(21).
作者姓名:陈钊正  郭晓峰  谭政宇  张薇
作者单位:江西省高速公路联网管理中心;华东交通大学软件学院
基金项目:江西省交通科技研究计划(2015X0042)
摘    要:当前人脸识别技术普遍存在识别精度低、抗干扰能力差的问题,为此,设计了基于省域路网的高速公路联网监控与人脸识别技术改进。介绍了高速公路联网监控系统设计的目的和需具备的功能。给出系统总体结构,其主要包括控制中心与外场终端设备,外场终端设备利用省域路网和控制中心设备进行通信,以此完成监控信息的采集与发布;通过透视投影成像阶段和摄像机标定阶段完成对省域路网视频的预处理,构建光照补偿模型和人脸特征检测模型,最终实现人脸识别技术的优化。实验结果表明,所设计方法相对于传统方法,就有较高的识别精度、其识别处理速度快、抗干扰能力强。

关 键 词:省域路网  高速公路  联网  人脸识别  
收稿时间:2018/2/5 0:00:00
修稿时间:2018/4/16 0:00:00

Improvement of Highway Network Monitoring and face recognition Technology based on Provincial Road Network
CHEN Zhao-zheng,GUO Xiao-feng,TAN Zheng-yu and ZHANG Wei.Improvement of Highway Network Monitoring and face recognition Technology based on Provincial Road Network[J].Science Technology and Engineering,2018,18(21).
Authors:CHEN Zhao-zheng  GUO Xiao-feng  TAN Zheng-yu and ZHANG Wei
Institution:1.Jiangxi Expressway Networking Management Center;2.Software college,East China Jiaotong University,Jiangxi Expressway Networking Management Center,Jiangxi Expressway Networking Management Center,Software college,East China Jiaotong University
Abstract:At present, face recognition technology generally exists the problem of low recognition accuracy and poor anti-interference ability. For this reason, an improved technology of freeway network monitoring and face recognition based on provincial road network is designed. The purpose of the design of the expressway network monitoring system and the functions needed are introduced. The overall structure of system is given, which mainly comprises a control center and field terminal equipment, terminal equipment field by provincial road network and control center equipment for communication, to complete the monitoring information collection and dissemination; through perspective projection stage and camera calibration stage pretreatment on provincial road network video, building illumination optimization to achieve face recognition technical compensation model and facial feature detection model. The experimental results show that the proposed method has high recognition accuracy, fast recognition processing speed and strong anti-interference ability compared with the traditional method.
Keywords:provincial road network  highway  network  face recognition  
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