Recognition of supplementary signs for correct interpretation of traffic signs

Traffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary sign(s) placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. The main difficulty of supplementary sub-sign recognition is the potentially infinite number of classes, as nearly any information can be written on them. In this paper, we propose and evaluate a hierarchical approach for recognition of supplementary signs, in which the “meta-class” of the sub-sign (Arrow, Pictogram, Text or Mixed) is first determined. The classification is based on the pyramid-HOG feature, completed by dark area proportion measured on the same pyramid. Evaluation on a large database of images with and without supplementary signs shows that the classification accuracy of our approach reaches 95% precision and recall. When used on output of our sub-sign specific detection algorithm, the global correct detection and recognition rate is 91%.