{"title":"基于深度学习的yolo和深度排序算法在城市交通管理系统中的车辆检测与计数","authors":"Rahul Kejriwal, Ritika H J, Arpit Arora, Mohana","doi":"10.1109/ICEEICT53079.2022.9768653","DOIUrl":null,"url":null,"abstract":"Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Vehicle Detection and Counting using Deep Learning basedYOLO and Deep SORT Algorithm for Urban Traffic Management System\",\"authors\":\"Rahul Kejriwal, Ritika H J, Arpit Arora, Mohana\",\"doi\":\"10.1109/ICEEICT53079.2022.9768653\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. 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引用次数: 2
摘要
车辆计数是估算道路交通密度以评估智能交通系统交通状况的过程。由于高端摄像机和技术的可用性,实时交通管理系统最近变得流行起来。目前的交通管理系统主要集中在速度检测、信号跳变、斑马线等方面,但对交通密度的估计还不够。提出了一种基于视频的车辆计数和跟踪方法,使用闭路电视和手持移动摄像机捕获的视频。该系统可用于智能城市创建智能交通灯信号,其中每个信号的持续时间取决于特定车道上的实时车辆密度。车辆计数分两步进行:将捕获的视频发送到基于YOLO (You Only Look Once)的深度学习框架,对车辆进行检测、计数和分类。采用深度排序算法对视频帧中的车辆进行多车跟踪。使用谷歌协作实验室对模型进行了六个不同类别的训练。考虑实施与印度道路环境有关的车辆数据集。对模型的性能进行了分析,提出的模型经过测试,平均计数准确率为86.56%,平均精度为93.85%。该模型可用于确定道路上的交通密度,并为当局的基础设施发展提供知识。开发智能化、智能化的交通监控系统也可以作为智慧城市项目的组成部分。
Vehicle Detection and Counting using Deep Learning basedYOLO and Deep SORT Algorithm for Urban Traffic Management System
Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.