Smart Traffic Management System using Image Processing

The objective of this research work is to develop a traffic management system that automates the current process which is carried out manually. It is divided into 3 modules - simulation to determine the traffic density, detect a crash/accident, detect ambulance using image processing and machine learning techniques. In the first phase, we determined traffic density to minimize the delay caused by traffic congestion and to provide the smooth flow of vehicles. The density of vehicles on each side can be identified by using datasets. If the density is low on a particular side, the period for that side is normal and if the density is high the period will automatically increase compared to normal density. In the second phase, we simulated a crash or accident detection and for the prototype consideration, we have used static accidental image and trained model. In the third phase, analyzed ambulance detection using the dataset, for the prototype consideration for this, used static ambulance image and trained dataset. On detection of an ambulance, the traffic light is automatically changed to green. In each phase, the data updating and monitoring are provided. This scheme is fully automated and identifies the emergency vehicle and controls the traffic lights dynamically. All of these processes are carried out with the help of image processing.