Abstract Traffic sign classification is a significant issue in the intelligent vehicle domain, which helps vehicles to follow the traffic rules and ensure the safety. Feature selection and description are very important and difficult for classification. In this paper, a novel traffic sign classification method is proposed which is based on the Latent Dirichlet Allocation (LDA) model. Feature topics are modeled based on various traffic signs by the LDA automatically. And traffic signs captured onboard are classified according to the modeled features. The experiment results show the efficiency of our work.


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    Titel :

    Traffic Sign Classification Base on Latent Dirichlet Allocation


    Beteiligte:
    Song, Lei (Autor:in) / Liu, Zheyuan (Autor:in) / Zhang, Xiaoteng (Autor:in) / Duan, Huixian (Autor:in) / Liu, Na (Autor:in) / Dai, Jie (Autor:in)


    Erscheinungsdatum :

    01.01.2018


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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