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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Bibliographic Details
Published in:Computing Vol. 103; no. 2; pp. 211 - 230
Main Authors: Al-qaness, Mohammed A. A., Abbasi, Aaqif Afzaal, Fan, Hong, Ibrahim, Rehab Ali, Alsamhi, Saeed H., Hawbani, Ammar
Format: Journal Article
Language:English
Published: Vienna Springer Vienna 01.02.2021
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-020-00869-8