An improved YOLO-based road traffic monitoring system

The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent mon...

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Vydáno v:	Computing Ročník 103; číslo 2; s. 211 - 230
Hlavní autoři:	Al-qaness, Mohammed A. A., Abbasi, Aaqif Afzaal, Fan, Hong, Ibrahim, Rehab Ali, Alsamhi, Saeed H., Hawbani, Ammar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.02.2021 Springer Nature B.V
Témata:	Artificial Intelligence Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Information Systems Applications (incl.Internet) Infrared detectors Modernization Monitoring Neural networks Roads & highways Sensors Sequences Software Engineering Special Issue Article Tracking systems Traffic Traffic management Traffic surveillance Transportation networks Vehicles Computer vision Intelligent traffic YOLOv3 Neural network Traffic analysis
ISSN:	0010-485X, 1436-5057
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Abstract	The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent monitoring of traffic involves the detection and tracking of vehicles on roads and highways. There are various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others. In this paper, we present an intelligent video surveillance-based vehicle tracking system. The proposed system uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles. We train the proposed system with different datasets. Moreover, we use real video sequences of road traffic to test the performance of the proposed system. The evaluation outcomes showed that the proposed system can detect, track, and count the vehicles with acceptable results in changing scenarios.
AbstractList	The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent monitoring of traffic involves the detection and tracking of vehicles on roads and highways. There are various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others. In this paper, we present an intelligent video surveillance-based vehicle tracking system. The proposed system uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles. We train the proposed system with different datasets. Moreover, we use real video sequences of road traffic to test the performance of the proposed system. The evaluation outcomes showed that the proposed system can detect, track, and count the vehicles with acceptable results in changing scenarios.
Author	Alsamhi, Saeed H. Abbasi, Aaqif Afzaal Ibrahim, Rehab Ali Al-qaness, Mohammed A. A. Fan, Hong Hawbani, Ammar
Author_xml	– sequence: 1 givenname: Mohammed A. A. orcidid: 0000-0002-6956-7641 surname: Al-qaness fullname: Al-qaness, Mohammed A. A. email: alqaness@whu.edu.cn organization: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University – sequence: 2 givenname: Aaqif Afzaal surname: Abbasi fullname: Abbasi, Aaqif Afzaal organization: Department of Software Engineering, Foundation University – sequence: 3 givenname: Hong surname: Fan fullname: Fan, Hong organization: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University – sequence: 4 givenname: Rehab Ali surname: Ibrahim fullname: Ibrahim, Rehab Ali organization: Department of Mathematics, Faculty of Science, Zagazig University – sequence: 5 givenname: Saeed H. surname: Alsamhi fullname: Alsamhi, Saeed H. organization: Software Research Institute, Athlone Institute of Technology – sequence: 6 givenname: Ammar surname: Hawbani fullname: Hawbani, Ammar organization: School of Computer Science and Technology, University of Science and Technology of China
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Snippet	The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely...
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SubjectTerms	Artificial Intelligence Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Information Systems Applications (incl.Internet) Infrared detectors Modernization Monitoring Neural networks Roads & highways Sensors Sequences Software Engineering Special Issue Article Tracking systems Traffic Traffic management Traffic surveillance Transportation networks Vehicles
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Title	An improved YOLO-based road traffic monitoring system
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