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Fusion: Practice and Applications
Volume 7 , Issue 2, PP: 91-99 , 2022 | Cite this article as | XML | Html |PDF


Driver Drowsiness Detection in Real-time

Authors Names :   Daksh Khetan   1 *     Arun Nawani   2     Anshul Aggarwal   3     Ms. Surinder Kaur   4  

1  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  2012daksh@gmail.com

2  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  arunnawani07@gmail.com

3  Affiliation :   Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  anshul.agg1510@gmail.com

4  Affiliation :   Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  kaur.surinder@bharatividyapeeth.edu

Doi   :   https://doi.org/10.54216/FPA.070203

Received: January 13, 2022 Accepted: April 09, 2022

Abstract :

In modern life, drowsiness is one of the major causes of road accidents, many of which are fatal. Analyzing statistics, it can be assumed that most road accidents occur as a result of drowsiness leading to serious injury and death. For this reason, various studies have been done on designing programs that can detect driver fatigue and alert them before a serious error occurs. This prevents them from falling asleep and having an accident. Some of the most common methods use automotive-based methods to design their own system. But these traditional measures were strongly influenced by other factors such as road structure, vehicle type and driver-wheel driveability. Some methods use psychological methods of their system that often provide the most accurate and consistent results in the driver's drowsiness monitoring. However, such techniques are very tedious as the electrodes need to be placed on the head and body. In addition, few studies are available where independent measurements are used as system installation, but such methods can confuse the driver and lead to unintended consequences. In this paper, we have proposed a non-disruptive and real-time program. Our proposed system classifies it as sleep deprivation. The model is fed with a large database of closed eyes and open eyes to produce results. The driver is notified by Buzz every time he is found drowsy. In our model, we use a standard forward-looking smartphone camera and use the information we have gained to produce results on our website. This can be more economical than using additional hardware.

Keywords :

drowsy driving , facial landmark , image processing , face detection , eye detection , alert


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Cite this Article as :
Daksh Khetan , Arun Nawani , Anshul Aggarwal , Ms. Surinder Kaur, Driver Drowsiness Detection in Real-time, Fusion: Practice and Applications, Vol. 7 , No. 2 , (2022) : 91-99 (Doi   :  https://doi.org/10.54216/FPA.070203)