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
Design a Hybrid Approach for the Classification and Recognition of Traffic Signs Using Machine Learning
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Classification and Recognition of Traffic Signs Using Deep Learning
Springer Verlag | 2022
|Traffic signs recognition with deep learning
IEEE | 2018
|Wiley | 2022
|Classification of Traffic Signs
Springer Verlag | 2017
|