摘要
智能交通系统(Intelligent Traffic System,ITS)是目前公路运输信息化、智能化的主流解决方案,其应用依赖于车流量检测数据的准确度。精准的车流量信息在通过智能交通系统改进车辆通行情况的过程中发挥重要作用。文章针对汽车流量检测情况,提出了基于机器视觉的卡尔曼(Kalman)跟踪检测方法,收集车辆运行中的相关信息,并对数据信息进行归类整理,通过车流量信息特征数据分析,对汽车通行情况进行有效分析,改进原有车流量计算中的错检以及误检问题,从而降低了车流计数的漏检率。
Intelligent transportation system(ITS)is currently the mainstream solution of road transportation informatization and intelligence,and its application depends on the accuracy of traffic flow detection data.Accurate traffic flow information can play an important role in the intelligent transportation system to improve vehicle traffic.In this paper,a machine vision-based Kalman tracking detection method is proposed for the detection of vehicle flow,which collects relevant information in vehicle operation,classifies and sorts out the data information,effectively analyzes the vehicle traffic situation through the analysis of vehicle flow information characteristics,and improves the error detection and false detection problems in the original traffic flow calculation,thereby reducing the missed detection rate of traffic flow counting.
作者
王墨琦
Wang Moqi(Intelligence and Information Engineering College,Tangshan University,Tangshan 063000,China)
出处
《无线互联科技》
2023年第7期109-111,共3页
Wireless Internet Technology
基金
唐山市科技计划项目,项目编号:13130247z。
关键词
机器视觉
车流量统计
卡尔曼检测方法
交通状态检测
machine vision
vehicle flow detection
Kalman detection method
traffic state detection
