A novel fuzzy background subtraction method based on cellular automata for urban traffic applications

Computational structure of cellular automata has attracted researchers and vastly been used in various fields of science. They are especially suitable for modeling natural systems that can be described as massive collections of simple objects interacting locally with each other, such as motion detection in image processing. On the other hand, extraction of moving objects from an image sequence is a fundamental problem in dynamic image analysis. Nowadays background modeling and subtraction algorithms are commonly used in real-time urban traffic applications for detecting and tracking vehicles and monitoring streets. In this paper by the use of cellular automata, a novel fuzzy approach for background subtraction with a particular interest to the problem of vehicle detection is presented. Our experimental results demonstrate that fuzzy-cellular system is much more efficient, robust and accurate than classical approaches.