Detection and Recognition of Traffic Sign Boards using Random Forest Classifier

Abstract

The traffic sign recognition system is a vital aspect of an intelligent transportation system since it provides information to drivers to help them drive more safely and effectively. This paper addresses some of these concerns, which will be accomplished in two steps. The first is the detection of traffic signs, which is divided into two stages. After a picture has been preprocessed to emphasize relevant information, signs are segmented based on color thresholding, shape-based detection. The second task is the recognition of traffic signs. There are two steps involved in this method. In this study, Histogram of Oriented Gradient is utilized as a feature extractor, and Random Forest Classifier is used in the recognition stage. The findings of the experiment show that utilizing Random Forest Classifier resulted in an accuracy score of 95.59 %, precision of 97.55 %, recall of 95.37% in the recognition process and 90.34 % accuracy in the detection process.