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

The real-time measurement of queue parameters is required in many trac situations such as accident and congestion monitoring and adjusting the timings of the trac 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 trac conditions and is able to provide better results than the traditional image processing algorithms. Ó 1999 Elsevier Science B.V. All rights reserved.