Digitized Image Processing Technique For Traffic Jam Detection

Abstract: Increased highway congestion and issues with conventional detectors have sparked interest in vehicle detection technology such as video image processing/Commercial image processing systems performed well in free-flowing traffic, but they struggle in congested locations. Vehicles appear differently under varied lighting and climatic circumstances, which are one of the existing traffic, jam identification challenges. As a result, current technologies have a difficult time distinguishing between traffic on highways and traffic on city streets. As a result, the goal of this research is to create a feature-based tracking system that can recognize cars in these difficult settings. Rather than watching complete vehicles, vehicle attributes are tracked to allow the system to quickly determine traffic conditions. We tested all of the photographs on our three levels of the programme, including Grey scale, under varied traffic and non-traffic scenarios. We tested all of the sample images on our three levels of the application, which are Grey scale level, Eigen Matrix level, and Eigen Vector level, under varied traffic and non-traffic scenarios. After we've accomplished all three levels, we'll try to figure out what the traffic situation is in that region.

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