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@article{155051,
        author = {Shelke Nilkanth Balasaheb and Khebade Akshay Ramesh and Kardile Kiran Satish and Kolase S.R.},
        title = {DRIVER DROWSINESS ALERT DETECTION FOR VEHICLE ACCELERATION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1201-1204},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155051},
        abstract = {Many people find it difficult to relax and get a decent night's sleep at night. Drivers who are sleep-deprived are more probable to fall asleep behind the wheel, growing the likelihood of an accident. The driver assistance system is presented in this system with the goal of reducing the frequency of accidents caused by driver fatigue and thereby improving road safety. Based on optical information and artificial intelligence, the proposed system treats the automatic identification of facial and driver fatigue. The proposed system estimates the distance between the eye iris and neck angle by locating, tracking, and analyzing both the driver's face and eyes. The location of the eye and neck, as well as the activities of those being seen, are all taken into account. It is used to determine how far the eye iris angle is from the neck angle in the eye and neck angle-based approach. },
        keywords = {driver safety; drowsiness detection; Image processing; alert system, etc.},
        month = {},
        }