A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to ac...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Cheng-Jian Lin, Shiou-Yun Jeng, Hong-Wei Lioa
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/ecc098a3d5a24c59b846c46caeaca029
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ecc098a3d5a24c59b846c46caeaca029
record_format	dspace
spelling	oai:doaj.org-article:ecc098a3d5a24c59b846c46caeaca0292021-11-15T01:20:10ZA Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO1563-514710.1155/2021/1577614https://doaj.org/article/ecc098a3d5a24c59b846c46caeaca0292021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1577614https://doaj.org/toc/1563-5147In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.Cheng-Jian LinShiou-Yun JengHong-Wei LioaHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Cheng-Jian Lin Shiou-Yun Jeng Hong-Wei Lioa A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
description	In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.
format	article
author	Cheng-Jian Lin Shiou-Yun Jeng Hong-Wei Lioa
author_facet	Cheng-Jian Lin Shiou-Yun Jeng Hong-Wei Lioa
author_sort	Cheng-Jian Lin
title	A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
title_short	A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
title_full	A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
title_fullStr	A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
title_full_unstemmed	A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
title_sort	real-time vehicle counting, speed estimation, and classification system based on virtual detection zone and yolo
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ecc098a3d5a24c59b846c46caeaca029
work_keys_str_mv	AT chengjianlin arealtimevehiclecountingspeedestimationandclassificationsystembasedonvirtualdetectionzoneandyolo AT shiouyunjeng arealtimevehiclecountingspeedestimationandclassificationsystembasedonvirtualdetectionzoneandyolo AT hongweilioa arealtimevehiclecountingspeedestimationandclassificationsystembasedonvirtualdetectionzoneandyolo AT chengjianlin realtimevehiclecountingspeedestimationandclassificationsystembasedonvirtualdetectionzoneandyolo AT shiouyunjeng realtimevehiclecountingspeedestimationandclassificationsystembasedonvirtualdetectionzoneandyolo AT hongweilioa realtimevehiclecountingspeedestimationandclassificationsystembasedonvirtualdetectionzoneandyolo
_version_	1718428956989849600

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

Ejemplares similares