Abstract:
Traffic signals are essential to guarantee safe driving at road intersections. However, they disturb and reduce the traffic fluency due to the queue delay at each traffic flow. In this work, an Intelligent Traffic Light Controlling (ITLC) algorithm. This algorithm considers the real-time traffic characteristics of each traffic flow that intends to cross the road intersection of interest, whilst scheduling the time phases of each traffic light. The introduced algorithm aims at increasing the traffic fluency by decreasing the waiting time of traveling vehicles at the signalized road intersections. Moreover, it aims to increase the number of vehicles crossing the road intersection per second.Firstly, the basic principle of moving object detecting is given by the Background Subtraction algorithm. Then, a self-adaptive background model that can update automatically and timely to adapt to the slow and slight changes of natural environment is detailed. In proposed system ithave performed background subtraction for extracting moving object by using (SVM) Support Vector Machine algorithms. The subjects are moving in different angles to the camera plane, on different image scales. This trained SVM is the core of a human detection algorithm which searches optical flow images for human-like motion patterns. A SVM builds a set of hyper planes in a high or multi-dimensional space can be used for regression and classification. High quality of the separation is attained by generating a functional-margin which provides the greatest distance between the nearest training input data points.
Keywords: Intelligent Traffic Light Controlling, signalized road intersections,self-adaptive background,support vector machine,Background subtraction.