A neural-vision based approach to measure traffic queue parameters in real-time

Abstract

The real-time measurement of queue parameters is required in many traffic situations such as accident and congestion monitoring and adjusting the timings of the traffic lights. Previous methods proposed by researchers for queue detection are based on traditional image processing algorithms. The method proposed here is based on applying the combination of edge detection and neural network algorithms. The edge detection technique is used to detect vehicles and estimate the motion, while neural network is used to measure the queue parameters. The neural network is trained for various road traffic conditions and is able to provide better results than the traditional image processing algorithms.