Automatic road detection from gray-level images in Wide Area Surveillance

Abstract

Wide Area Surveillance (WAS) systems are capable of providing continuous surveillance of critical areas as wide as city center (approximately 20 km square), mostly as a gray-level video. Utilization of road information for WAS systems increases moving vehicle tracking performance, while reducing the false alarm rates that might occur due to tall buildings, shadows or terrain. Two different novel approaches for automatic road detection from gray values images are presented in this paper. In the first approach, a probabilistic model for the road pixels of a gray-scale WAS image is obtained by utilizing parallel line detection and tubularity estimation. In the second approach, these road probabilities are converted into a graph representation for local areas. These graphs are solved by using graph cut formulation which exploits min-cut, max-flow algorithm. As a result of this solution, the road mask is extracted by applying a hierarchical model that results with a transition from local to global representation. Although the methods in literature mostly utilize multi-spectral images that result wih a smaller resolution yielding surveillance of a limited region, the proposed method uses gray-scale images which enable surveillance of much wider areas. The proposed method was tested some high resolution WAS images and resulted with promising results.