The traffic density on roads has been increasing rapidly for the past few decades, which has in turn been reflected in the increase in traffic violations and accidents. Official reports from various governments and private entities bolster the fact that, indeed, the current methods for traffic monitoring are inept to deal with the huge traffic density [1, 2]. These methods, which traditionally included the deployment of traffic police personnel at a select few junctions where the traffic density is high, ignore the majority of the other roads. Traffic monitoring systems that exploit image processing, computer vision and deep learning techniques thus come out to be a viable and optimal solution to monitor traffic and detect violations. These systems can easily be integrated with the architecture of law enforcement to penalize violators in real time. The proposed method—which utilizes YOLOv3 and SORT—is effective and accurate in detecting several violations like—over-speeding, wrong-way driving, signal jumping, driving without helmet and triple seat violation. It also helps to keep track of the count of vehicles, their types and also the number of axels for multi-axle vehicles, thus, asserting itself as a novel and indigenous solution to a widely recognized problem.
Traffic Monitoring and Violation Detection Using Deep Learning
Lect. Notes Electrical Eng.
International Conference on Big Data, Machine Learning, and Applications ; 2021 ; Silchar, India December 19, 2021 - December 20, 2021
2023-11-30
12 pages
Article/Chapter (Book)
Electronic Resource
English
Traffic monitoring , Traffic violations , Computer vision , Deep learning , CNN , YOLOv3 , Object tracking Computer Science , Computer Systems Organization and Communication Networks , Computer System Implementation , Computer Communication Networks , Special Purpose and Application-Based Systems , Artificial Intelligence
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