Circular Road Signs Recognition with Affine Moment Invariants and the Probabilistic Neural Classifier

摘要

In this paper the neural classifier for recognition of the circular shaped road signs is presented. This classifier belongs to the road signs recognition module, which in turn is a part of a driver assisting system. The circular shaped prohibition and obligation signs constitute the very important groups within the set of road signs. In this case however, it is not possible for a detector to determine rotation of the shapes that would allow dimension reduction of the search space. Thus the classifier has to be able to properly work with all possible affine deformations. To alleviate this problem we propose to use as features the statistical moments which were shown to be invariant within an affine group of transformations. The classification is performed by the probabilistic neural network which is trained with sign examples extracted from the real traffic scenes. The obtained results show good accuracy of classification and fast operation time.