Analysis and estimation of traffic density: an efficient real time approach using image processing

Abstract

Nowadays, traffic density is very high in most of the urban areas, because of the increase in the number of vehicles. Traffic congestion is a very common problem that leads to more lay-out time in traffic. In order to address this issue, an algorithm has been proposed in this work for traffic flow monitoring and analysis in real time based on image processing techniques. This paper describes a method of real time area and frame based traffic density estimation using edge detection for intelligent traffic control system. Area occupied by the edges of vehicles will be considered to estimate traffic density. The system will automatically estimate the traffic density of each road by calculating the area occupied by traffic which in turn will help to determine the duration of each traffic light. The main role of this study lies in the development of a new technique that detects traffic density according to the area occupied by the edges of vehicles for controlling traffic congestion. The proposed algorithm was evaluated on a 30 s video dataset which was sampled into 8 frames and yielded an average accuracy of 98.07%. This is comparable with the existing algorithms in the literature.