Robust segmentation process to detect incidents on highways

Abstract

In this paper a robust segmentation process for detecting incidents on highways is presented. This segmentation process is based on background subtraction and uses an efficient background model initialisation and update to work 24/7. A cross-correlation based shadow detection is also used for minimising ghosts. It is also proposed a stopped vehicle detection system based on the pixel history cache. This methodology has proved to be quite robust in terms of different weather conditions, lighting and image quality. Some experiments carried out on some highway scenarios demonstrate the robustness of the proposed solution. © 2008 Springer-Verlag Berlin Heidelberg.