Traffic Prediction for intelligent Transportation system using Machine Learning

The Project ” Traffic Prediction for Intelligent Transportation system using Machine Leaning” and counting the number of vehicles present in a given image or video. Once the vehicles are detected, they can be counted using various techniques such as tracking or counting the number of bounding boxes around the detected vehicles. These techniques can be further improved by using multiple cameras or sensors to cover larger areas and reduce errors. Overall, vehicle detection and counting is an important task in computer vision that has various applications in traffic management, surveillance, and autonomous driving. Currently the traffic control system in our country is non-flexible to the ever growing number of vehicles on the road. Traffic light is the basic element in traffic flow control through specified waiting and going time, fixed traffic light time systems is a bad control way. Intelligent traffic system includes smart way to control traffic light time based on number of vehicles in each lane. Improving traffic signal control system will increase safety, reliability, and traffic flow speed and reduce average travelling and waiting time for passengers. The objective is to design an efficient automatic Traffic Time Saver system. The system is implemented on the traffic control. In this proposed application system first captures the vehicle image. Vehicle image is extracted using the image segmentation finally converting the images from RGB to gray scale. Next, the segmentation is applied on the prepared image and then for each segment the neural networks.