A Self-Intelligent Traffic Light Control System based on Traffic Environment using Machine Learning
In this paper, we will detect and track vehicles on a video stream and count those going through a defined line and to ultimately give an idea of what the real-time on street situation is across the road network. Our major objective is to optimize the delay in transit of vehicles in odd hours of the day. It uses YOLO object detection technique to detect objects on each of the video frames And SORT (Simple Online and Realtime Tracking algorithm) to track those objects over different frames. Once the objects are detected and tracked over different frames a simple mathematical calculation is applied to count the intersections between the vehicles previous and current frame positions with a defined line. At present, the traffic control systems in India, lack intelligence and act as an open-loop control system, with no feedback or sensing network. Present technologies use Inductive loops and sensors to detect the number of vehicles passing by.