A Method for Recognizing Prohibition Traffic Sign Based on HOG-SVM

Abstract

In order to recognize prohibition traffic signs, based on the analysis of the color occupancy of prohibition traffic signs, this paper proposes a method to recognize the prohibition traffic signs based on the feature of Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM). The recognition method is mainly divided into three steps: the first step is image preprocessing, which realizes the size normalization processing, grayscale processing and Gamma correction of the image; the second step is the feature extraction of HOG; the third step is the recognition of prohibition traffic signs based on SVM. In the design and implementation of the prohibition traffic sign classifier, the prohibition traffic sign image training after linear transformation is used to train 42 binary classifiers, and then based on these 42 classifiers, the prohibition traffic sign classifier is constructed and implemented. Finally, the self-built data set was used to test and analyze the prohibition traffic sign recognition method, and the overall recognition accuracy rate was 90.2%.