Testing feature selection in traffic signs

Abstract

Road signs carry essential information for successful driving. Therefore, if we are interested in developing a Driver Support Systems, both, detection and classification of road signs are essential tasks for an autonomous system. However, both tasks are some of the less studied subjects in the field of Intelligent Transport systems. In this research we lay the foundations of a software implementation for a classifier system that will be implemented in hardware and will be able to be used for real-time traffic sign categorization. The selected classification method is a Multilayer Perceptron trained with Back-Propagation algorithm. The reason of this selection is, on one hand, that for certain types of problems, such as object recognition in natural environments, neural network learning methods provide a robust approach. On the other hand, and under certain, limitations related mainly to the number of units, a hardware implementation on FPGA of ANN is possible. Therefore, ANNs are a good method for real-time processing in real-word problems