Traffic Signs Recognition using HP and HOG Descriptors Combined to MLP and SVM Classifiers

Detection and recognition of traffic signs in a video streams consist of two steps: the detection of signs in the road scene and the recognition of their type. We usually evaluate globally this process. This evaluated approach unfortunately does not allow to finely analyze the performance of each step. It is difficult to know what step needs to be improved to obtain a more efficient system. Our previous work focused on a real-time detection of road signs, by improving the performances of the detection step in real time. In this paper, we complete the work by focusing on recognition step, where we compare the performances between histogram projection (HP) descriptor, and the histogram-oriented gradient (HOG) descriptor combined with the Multi-Layer Perceptron (MLP) classifier, and the Support Vector Machine (SVM) classifier, to compute characteristics and descriptors of the objects extracted in the step of detection, and identify the kind of traffic signs. Experimental results present the performances of the four combinations of these methods “Descriptor-Classifier” to identify which of them could have high performance for traffic sign recognition. Keywords—Traffic signs detection and recognition; Histogram of oriented gradient (HOG); Support Vector Machine (SVM); Histogram projection (HP); Multi-layer perceptron (MLP)