This paper focuses on the development of a non-intrusive driver warning system as part of ADAS (Advanced Driver Assistance Systems) to help improve the safety of all road users, when driving, on the road. The proposed algorithm uses computer vision, implemented based on facial landmark detection, to detect driver drowsiness based on the driver’s eye condition. This algorithm has shown good results with HOG + Linear SVM for searching and locating faces in the image, as well as determining the eye condition of the driver with and without glasses. If the eyes remain closed longer than expected or if the driver is not looking straight ahead, it is an indication that the driver is drowsy or tired, the system then sends a warning signal to the driver.


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    Titel :

    Tracking of Driver Behaviour and Drowsiness in ADAS


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arseniev, Dmitry G. (Herausgeber:in) / Aouf, Nabil (Herausgeber:in) / Evstafev, Oleg (Autor:in) / Shavetov, Sergey (Autor:in)

    Kongress:

    International Conference Cyber-Physical Systems and Control ; 2021 ; St. Petersburg, Russia June 29, 2021 - July 02, 2021



    Erscheinungsdatum :

    2023-01-21


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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