Traffic congestion has been increasing because of increased population growth mainly in major cities due to urbanization. Traffic congestion causes increased air pollution, travel time and mostly traffic accidents; therefore we need an efficient traffic management system. Today most of the cities of the world have intelligent transport system which is equipped with electronics devices to communicate about the traffic condition with the moving vehicle and also monitor the traffic rules and regulation. Current traffic control techniques involving magnetic loop detectors buried in the road, infra-red and radar sensors on the side provide limited traffic information and require separate systems for traffic counting and for traffic surveillance. The disadvantages of the existing system are that it requires traffic personnel to monitor the traffic and magnetic loop detectors cause's high failure rate when installed on the road surfaces. In contrast, video-based systems offer many advantages compared to traditional techniques. They provide more traffic information, combine both surveillance and traffic control technologies, are easily installed, and are scalable with progress in image processing techniques. Implementation of the project will eliminate the need of traffic personnel at various junctions for regulating traffic. Thus the use of this technology is valuable for the analysis and performance improvement of road traffic. Traffic monitoring based on density of vehicles improve the traffic control system by calculating the density of vehicles on the road. This Project proposes to control the traffic using image processing algorithms and embedded systems to control the traffic signals. The videos taken by a camera is analyzed using Background Subtraction method to detect, track and count the number of vehicles moving in each lane to obtain the most efficient traffic management. The vehicle classification and speed detection is also done, such that vehicles are classified into heavy vehicle (trucks, bus) and low vehicle (bikes, cars) and the over speed vehicles are detected and indicated by red boundary.

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Back Ground Subtraction Loop Detectors And Feature Extraction.