A novel region of interest selection approach for traffic sign recognition based on 3D modelling

Most existing vision-based traffic sign recognition (TSR) systems rely for the initial shape detection on a processing step that assumes possible positions and sizes for traffic signs to be known a priori. Usually the better the expected position and size can be estimated, the better do these shape detection algorithms perform concerning accuracy and processing time. This paper presents a novel region of interest concept that is based on the known real world positions of traffic signs during the different driving situations. The vehicle surrounding along the predicted host path and the expected positions of traffic signs are modelled in 3D. A projection to the image plane results in a region of interest for traffic signs. This projected region of interest reduces the search-space for initial candidate detection. A reduced search-space leads to a lower number of false positives and reduced computational costs.