Combined 2d/3d traffic signs recognition and distance estimation

Accidents caused by reduced concentration of drivers on traffic signs indications continue to represent an important part of accident-prone situations. Face to this threat, our work aims to develop a vision-based traffic sign recognition method based on a two-step recognition and 3D distance computing module. Firstly, a monocular color based segmentation method is applied to generate traffic sign candidates. Then, HoG features are applied to encode the detected traffic signs and compute the feature vector. This vector is used as an input to a SVM classifier to identify the traffic sign class. Secondly, a dense disparity map between the left and right images is created for the recognized traffic sign region to compute its distance to the vehicle carrying the stereovision. Our method affords high precision rates under different weather conditions. Moreover, it operates with a timing that is reasonable for real-time applications. The obtained results, compared to leading methods from the literature, prove the efficiency of our proposed method.