Robust Segmentation Process to Detect Incidents on Highways

In this paper it is presented a robust segmentation process for detecting incidents on highways. This segmentation process is based on background subtraction and uses an efficient background model initialization and update to work 24/7. A cross-correlation based shadow detection is also used for minimizing 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.