Road Traffic Monitoring Using Image Processing—A Survey of Systems, Techniques and Applications

Abstract

Following their earlier research work associated with the development of the TRIP System at Sheffield University/UMIST the authors are engaged in a re-evaluation of image processing applications in transportation engineering. Existing image processing techniques applied to traffic data collection involve only low level image interpretation of sequences of images. Although primitive these techniques have enabled useful parameters such as vehicle speed, counts and lane occupancy to be measured effectively. It is believed, however, that future image processing systems will require additional built-in intelligence to cope with more sophisticated applications.This paper will provide a review of the experience of a number of groups active in image processing research in the transport field. It will go on to comment upon the short-falls of existing equipment and processing techniques. These considerations were taken into account during the development of the TRIP II system, which will then be described. The paper will go on to suggest methods whereby some of the inadequacies of past systems may be overcome in the light of trends in computing equipment, special purpose hardware and enhanced processing technique and machine intelligence.In order to achieve near real-time operation past systems have concentrated upon applying differencing techniques to small samples of video images. Conventional systems have often been based on purpose built computers and application specific algorithms. However the TRIP II system takes full advantage of the area sensing capability of video images. The approach is different since the hardware is based upon an inexpensive IBM PC/AT microcomputer. The software provides an alternative means of processing complex pattern information by simulating the operation of a neural network. The vehicle detection algorithm relies on the ability of the TRIP II system to learn from examples and discriminate between complex patterns within video images of traffic scenes. Over the past decade many image processing systems for traffic monitoring have been designed and implemented. The paper will finally go on to outline the range of applications for such image analysis systems.