Sleepy and distracted drivers are increasingly being seen as major causes of fatal car accidents across the globe. This happens most often because the driver hasn't had enough sleep, but it may also be the consequence of untreated sleep disorders, substance abuse, or working odd hours. That's why it's crucial for cars to have systems in place to track and verify the identities of their drivers. Analyzing things like head orientation, eye movement, yawning, and ocular condition are crucial. This study suggests a mechanism for detecting driver fatigue by keeping track of the number of times the driver opens and closes their eyes and their lips. If the driver keeps his eyes closed for too long, an alarm will go off. In addition, if the driver is seen nodding off more than some few times, the vehicle's owner is contacted by e-mail to confirm that the driver is exercising precautions to prevent dozing off behind the wheel. The results of the system proposed in the paper on Deep learning technology of Dlib show that it is effective at detecting tiredness and thus decreasing the number of accidents that occur while driving. The system uses a CNN (Convolutional Neural Network) as its base algorithm for accurate detection and is implemented in MATLAB environments with a camera mounted. The suggested technique achieved a 99% success rate for video clip input.


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    Titel :

    Deep Learning based Driver Drowsiness Detection


    Beteiligte:
    Buddhi, Dharam (Autor:in) / Negi, Poonam (Autor:in)


    Erscheinungsdatum :

    2022-11-18


    Format / Umfang :

    468444 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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