Road sign recognition system based on GentleBoost with sharing features

Nowadays, the number of vehicles is growing rapidly, and more and more intelligent transportation systems are developed for assisting drivers. Road sign detection and recognition is extremely important for safe and careful driving, this system can not only inform the driver about the condition of the roadway but also support the driver during the tedious task of remembering the large number of road signs. In this work, we propose a fast road sign detection and recognition system. This system takes advantage of the HSI color space to filter most of the false alarms. Distance to borders (DtBs) and Support Vector Machine (SVM) are then used for the shape detection. Finally, the candidate blobs that pass through the shape detection is recognized by a GentleBoost with sharing features detector and rotation, scale, translation-invariant (RST-invariant) template matching. For recognition step, color information is used for training GentleBoost detector to ensure the accuracy of the system; and the achromatic part of the candidates are matched to the templates by RST-invariant template matching. The main advantage of this system is that it can detect and recognize road signs efficiently and accurately.