SMART SURVEILLANCE: AI-DRIVEN DRIVER DROWSINESS DETECTION SYSTEM

Yashika Khandelwal; Palak Gupta; Priyal Jain; Vartika Jain; Shazia Haque

International Journal of Computer Aided Manufacturing

SMART SURVEILLANCE: AI-DRIVEN DRIVER DROWSINESS DETECTION SYSTEM

Abstract

In this study, a real-time application for driver drowsiness detection based on machine learning and deep learning is developed. Accidents, including traffic accidents, are now a frequent cause of fatalities and injuries among people. One of the major contributing factors to traffic accidents and a significant threat to road safety is drowsy driving. As a result, there are more people dying and getting hurt every year. Serious traffic accidents have frequently resulted from fatigued driving. Such accidents can be avoided by detecting driver intoxication early and acting accordingly. When a driver is drowsy, they are halfway between asleep and awake. In addition to addressing all of these accident causes, the goal of this study is to resolve this issue. A system for the early detection of drowsiness symptoms must be created in order to prevent traffic accidents. Detected symptoms can be either psychological or physical. One of the IT industry's fastest-growing subsectors is artificial intelligence. The study of artificial intelligence focuses on how to teach a computer to perform tasks that humans currently perform. With Python3, we will create a machine learning and deep learning based artificial intelligence model for facial recognition

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How to cite

Yashika Khandelwal, Palak Gupta, Priyal Jain, Vartika Jain, Shazia Haque. SMART SURVEILLANCE: AI-DRIVEN DRIVER DROWSINESS DETECTION SYSTEM. International Journal of Computer Aided Manufacturing. 2023;9(1):29–35.

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SMART SURVEILLANCE: AI-DRIVEN DRIVER DROWSINESS DETECTION SYSTEM

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