The face, a crucial bodily feature, communicates a lot of information. Facial expressions, such as blinking and yawning more often than usual, differ from those in normal conditions when a driver is fatigued. A major contributing element in a significant proportion of auto accidents is driver inattentiveness. In recent years, this driver sleepiness detection method has received great praise and has been applied in several scenarios, such as visual attention monitoring and driver activity tracking. Studies show that inattentive and fatigued drivers are to blame for around 25% of severe highway fatal crashes, which is a lot more serious than drunk driving. In this paper, we suggest an algorithm that will see the face of the person who is driving and the eyes to be able to assess the degree of tiredness brought on by sluggish eye closure. Rapid prototyping of this participatory research and Innovation would be fruitful for sustainable communities.


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    Titel :

    Driver Drowsiness Detection Using Deep Learning


    Beteiligte:
    Jain, Anuj Kumar (Autor:in) / Sharma, Vikrant (Autor:in) / Goel, Sandeep (Autor:in) / Tiwari, Raj Gaurang (Autor:in) / Vajpayee, Amit (Autor:in) / Bhandari, Rahul (Autor:in)


    Erscheinungsdatum :

    2023-06-23


    Format / Umfang :

    1168155 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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