Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing

In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential...

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Bibliographic Details
Published in:Journal of advanced transportation 2024-05, Vol.2024 (1)
Main Authors: Villa, Jaime, García, Franz, Jover, Rubén, Martínez, Ventura, Armingol, José M.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Architecture
Cameras
Computer vision
Control systems
Data mining
Datasets
Deep learning
Edge computing
Infrastructure
Infrastructure (Economics)
Intelligent vehicles
Literature reviews
Localization
Machine learning
Machine vision
Monitoring
Monitoring systems
Neural networks
Sensors
Traffic control
Traffic management
Vehicles
Video data
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Summary:In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation.
ISSN:0197-6729
2042-3195
DOI:10.1155/2024/3679014