(Wasit Journal of Computer and Mathematics Science, 2023) Guma, Ali; Sadıkoğlu, Emre; Abdelhak, Hatim
Advanced Driver Assistance Systems (ADAS) are a fundamental part of various vehicles, and the
automatic classification of traffic signs is a crucial component. A traffic image is classified based on its recognizable
features. Traffic signs are designed with specific shapes and colours, along with text and symbols that are highly
contrasted with their surroundings. This paper proposes a hybrid approach for classifying traffic signs by combining
SIFT with SVM for training and classification. There are four phases to the proposed work: pre-processing, feature
extraction, training, and classification. A real traffic sign image is used for classification in the proposed framework,
and MATLAB is used to implement the framework