Video-based driver abnormal driver behavior detecting and risk control are incrementally well-liked nowadays, because it is important for ensuring the safety of drivers and people within the vehicles, and it is a necessary step in realizing automatic driving at this situation. Different researchers are proceeded with the detecting abnormal behavior of driver with the help of the capturing and detecting the face and objects in the hand of a driver through image and video process, but the standard algorithms are not efficient of detecting difficult temporary characteristics of driver behavior. In this paper, we have added a step to the existing mechanism which can track the user’s abnormal behavior and thus send the information to the owner of vehicle. We have used three deep learning models to detect the behavior of the driver. These three new fusion models help in designing the model building and thus helping in risk control and help traffic department.


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    Titel :

    Video-Based Abnormal Driving Behavior Detection and Risk Control Using Machine Learning


    Weitere Titelangaben:

    Algorithms for Intelligent Systems


    Beteiligte:
    Mandal, Jyotsna Kumar (Herausgeber:in) / Hinchey, Mike (Herausgeber:in) / Rao, K. Sreenivas (Herausgeber:in) / Sai Prasad, Kashi (Autor:in) / Pasupathy, S. (Autor:in)


    Erscheinungsdatum :

    14.09.2022


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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