Traffic Density Estimation in Traffic System Using Image Processing

: Due to the increase in the number of vehicles day by day, traffic congestions and traffic jams are very common these days. In the modern world urban centres are growing at a very high rate especially traffic jams have become routine. One method to overcome the traffic problem is to develop an intelligent traffic control system which is based on the measurement of traffic density on the road using real time video and image processing techniques. In this paper, a simple and elegant approach for estimating the road traffic density during daytime using image processing the video data collected is first broken into frames which is then processed in a series of steps finally the vehicle are detected and extracted from the images and counted. Then the traffic density is obtained as the number of vehicles per unit area of the road section. The proposed approach was implemented in MATLAB R2015a and average vehicle detection accuracies of 96.0% and 82.1% were achieved for fast moving and slow moving traffic scenes respectively. The theme of this is to control the traffic by determining the traffic density on each side of the road and control the traffic signal intelligently by using the density information. This paper presents the algorithm to determine the number of vehicles on the road. The density counting algorithm works by comparing the real time frame of live video by the reference image and by searching vehicles only in the region of interest (i.e. road area). The computed vehicle density can be compared with other direction of the traffic in order to control the traffic signal smartly. The use of traffic monitoring techniques based on image processing could be very useful. In this paper you will know the system that estimates the rates at an urban crossroad by processing the sequence of images taken by a video camera is presented. This type of estimation is segmentation, and moving object tracking techniques are used thus the system will automatically estimate the traffic density of each road which will help to determine the duration of each traffic light. An intelligent traffic signal control system with the proposed traffic density estimation technique will be far better than the conventional timer based system image captured with cameras will be used for the detection of traffic density for the intelligent traffic control system. As populations as well as number of vehicles are increasing in the cities and towns, traffic congestion has become a major problem for the time being. Delays, fuel consumption and air pollution are some of the problems arise from traffic. In this paper, we proposed a system to overcome some these problems by providing an alternate route for the vehicles by predicting a possible congestion ahead of that road. Many traffic light systems operate on a timing mechanism that changes the lights after a given interval. An intelligent traffic light system senses the presence or absence of vehicles and reacts accordingly. The idea behind intelligent traffic systems is that drivers will not spend unnecessary time waiting for the traffic lights to change. An intelligent traffic system detects traffic in many different ways: Millions of vehicles pass via roads and cities every day. Various economic, social and cultural factors affect growth of traffic congestion. The amount of traffic congestion has major impacts on accidents, loss of time, cost of money, delay of emergency, etc. Due to traffic congestions there is a loss in productivity from workers, people lose time, trade opportunities are lost, delivery gets delay, and thereby the costs goes on increasing. To solve these congestion problems, it is better to build new facilities and infrastructure but at the same time make it smart.