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@article{143666,
        author = {Bhakti Deshpande and Prof. R. U. Shekokar},
        title = {Methods of Classification of Traffic Signs Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {12},
        pages = {407-410},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143666},
        abstract = {Driver Assistance Systems refer to various high-tech in-vehicle systems that are designed to increase road traffic safety by helping drivers gain better awareness of the road and its potential hazards and also other drivers around them. This paper presents a comparative study of many classification methods for the task of recognizing traffic signs .These methods are k-nearest neighbors (kNN), artificial neural network (ANN), support vector machine (SVM), and random forest (RF). First, HSI-based process of color segmentation is applied to obtain selected regions. Using centroide based feature, these regions will be classified into three shape classes, such as triangle, rectangle and circle. Here after, histograms of oriented gradient (HOG) features are extracted from each region that will be utilized in recognizing step. This paper is intended to review different methods along with a brief survey of these various methods.},
        keywords = {traffic sign recognition; histogram of oriented gradient; artificial neural network; random forest; k-nearest neighbor; support vector machine.},
        month = {},
        }