Traffic signs perform essential functions on roadways. By paying attention to traffic signs, drivers may estimate their directions and vehicle speeds.. However, it is common for drivers to sometimes misunderstand the location and significance of traffic signals, which results in accidents. As a result, technical advancements in computer enable the creation of traffic sign detection system t. The positioning of traffic signs, the weather, other cars and billboards that block the view of the signs are some of the difficulties considered in this research. Traffic sign detection, is the first crucial phase of Traffic sign recognition. The proposed method works in detecting traffic sign with feature extraction of Histogram oriented gradient (HOG) with decision tree for color and gray-level co-occurrence matrix (GLCM) with decision tree for texture. Gray-level co-occurrence matrix with two different supervised classification algorithms Decision Tree and Random Forest in which Decision Tree algorithm gives maximum accuracy.


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    Titel :

    Traffic Sign Detection using HOG and GLCM with Decision Tree and Random Forest


    Beteiligte:
    J, Asha (Autor:in) / R, Giridhran (Autor:in) / K, Agalya (Autor:in) / R, Sathya (Autor:in)


    Erscheinungsdatum :

    13.12.2022


    Format / Umfang :

    3720618 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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