Intelligent Traffic Signal Control System Based On Traffic Density Estimation

Automatic video analysis from traffic surveillance cameras is a fast-emerging field based on computer vision and image processing techniques.It is a key technology for intelligent transport system(ITS)to ensure public safety.In recent years,the scope for automatic analysis of traffic activity has been increased.The method defines video analytics algorithms for computer-vision and image processing-based surveillance systems to extract appropriate information from video.In the traffic scenarios,there are numerous monitoring objectives which can be supported by the application of computer-vision,image processing and pattern recognition techniques,including the detection of traffic violations.Here,full-featured automatic system for vehicle detection,tracking and traffic signals control is presented.This system has many applications in machine vision and Traffic management and control.The main objective of this work is to present a system that solves the practical problem for traffic management and control system.All steps of the process,from video acquisition to traffic density estimation are considered to achieve an automatic traffic signal control system.Moving Object detection is one of the key step for activity analysis in video surveillance.It provides a classification of the pixels into either foreground or background.Various methods have been proposed by researchers for segmenting out foreground objects in a video sequence,each having their own merits and demerits.A good method should be robust to illumination changes,non-static background and camera noise.In this study we used two different techniques for moving object detection and tested with different videos to overcome illumination changes and camera noise.A comparative study is also shown to help experts choosing the method which best suites their application needs.Some morphological operations have also been used to remove unwanted object and then use blob detection techniques to compute connected components and links between the broken edges.Finally estimate the presence of traffic based on area covered the vehicles on the road instead of counting the numbers of vehicles for traffic signals to control or reduce the traffic congestion.