Road Traffic Update and Control Traffic Light using Dynamic Patterns from Video Streaming Data

Controlling traffic congestion is one of the most challenging issues in the real world, and an automated traffic monitoring system established on the computer vision and internet of things is an emerging scientific field in research. Traffic Congestion becomes severe in the most densely populated area to move timely from one place to another, passing through massive traffic. Although several research papers have presented their solutions for monitoring traffic congestion, we do not receive a permanent solution. In this paper, We try to find a different but unique way to overcome it. Our proposed system works with real-time video sequential data and detects different vehicle shapes using contour-based learning and a convex hull algorithm. We execute the procedure in a way where we work with per-second frames (fps) of the video, and then we determine the total, running, and motionless stagnant vehicles in each frame. After observing vehicle differentiation, an automated decision will be generated, i.e., JAM or NOT JAM which will be transmitted to the Map for users’ assistance. After that, the decision about the traffic situation of all nodes makes a combination, and then we find two patterns, one will identify the traffic situation of all connected roads, and another will control the traffic signal automatically.