Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning

Guma Ali; Emre Sadıkoğlu; Hatim Abdelhak

doi:10.31185/wjcms.151

Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning

Authors

Guma Ali Department of Computer Science and Electrical Engineering, Muni University, Arua, Uganda
Emre Sadıkoğlu Department of Computer Engineering, Yalova University, Yalova, Turkey
Hatim Abdelhak Dida University of belhadj bouchaib ain temouchant,Algeria

DOI:

https://doi.org/10.31185/wjcms.151

Keywords:

Hybrid Approach

Abstract

The automatic system for classifying traffic signs is a critical task of Advanced Driver Assistance Systems (ADAS) and a fundamental technique utilized as an integral part of the various vehicles. The recognizable features of a traffic image are utilized for their classification. Traffic signs are designed to contain specific shapes and colours, with some text and some symbols with high contrast to the background. This paper proposes a hybrid approach for classifying traffic signs by SIFT for image feature extraction and SVM for training and classification. The proposed work is divided into phases: pre-processing, Feature Extraction, Training, and Classification. MATLAB is used for the implementation purpose of the proposed framework, and classification is carried out by utilizing real traffic sign images

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