A neural network for image-based vehicle detection

Vehicle detection on roadways is useful for a variety of traffic engineering applications from intersection signal control to transportation planning. Traditional detection methods have relied on mechanical or electrical devices placed on top of, or embedded in, pavements. These systems are relatively expensive to install, tend to be unreliable over time, and are limited in their capabilities. Considerable research has been conducted in the area of machine vision for Wide Area Vehicle Detections Systems (WADS). These systems have typically employed conventional image processing and pattern matching algorithms, and many installations have been sensitive to varying lighting conditions, camera perspective, and shadows. In addition, these systems have often required large amounts of computing resources. This paper reports on the development of a new image based vehicle detection system that is based on a simple back propagation/feedforward neural network for tracking vehicles. Application of this concept in a field system is discussed and preliminary results are presented. These results suggest that the neural network vehicle tracking model can be used to reliably detect vehicles. In addition, the training capability of the neural network detection model permits the system to adapt to variations in lighting and camera placement. This should lead to simplified installation and maintenance of WADS.