This study proposes an automated traffic management system that employs image processing techniques, as well as edge and object detection algorithms. The system’s primary goal is to improve traffic flow and alleviate congestion by automatically detecting and analyzing vehicles in real-time using strategically placed cameras. Edge detection algorithms are used to identify object boundaries in the images captured by the cameras. Object detection algorithms are then utilized to identify and classify vehicles based on their size, shape, and other features. Machine learning algorithms are also used to enhance vehicle detection and classification accuracy. The system’s processed data is utilized to dynamically manage traffic signals, control traffic flow, and provide alternate routes to bypass congested areas. The proposed system has the potential to enhance traffic efficiency, reduce travel time, and promote road safety.
Automated Traffic Management Using Image Processing
2023-10-27
603532 byte
Conference paper
Electronic Resource
English
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