International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences
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IOT Based Driver Drowsiness Detection System

Authors: Kalpesh Shinde, Lalit Borse, Pankaj Khaire, Triveni Pise, Prof. Naresh Shende

Country: India

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Abstract: In recent years, drowsiness is the main cause of the accidents in India due to lack of sleep, tiredness and soon. In order to reduce the case of vehicle accidents caused by drowsiness of the driver is to detect them and warn them using an alarm. Many techniques, such as eye retina detection, have been used to detect sleepiness by facial features. Here in this paper, we propose a method for detecting the driver’s drowsiness by detecting the person’s closed eye for a few seconds. In this report, we propose a more accurate method for detecting drowsiness, by. The main contribution for this project is the drowsiness detection and warning, which is based on the person’s open or closed eye. This project discuss on how to detect the eyes of the driver from the real time environment using the webcam represents the dashboard camera in a car. By using the real time detection, author use the built-in laptop webcam to detect the eyes of the demonstrator. The drowsiness detection system will detect the open and closed eye. The designed system will detect the face area and the coordinate of the eye. Detecting the face area is narrow down to detect eyes within face area. Both left and right eyes will be framed out once it found. The parameters of the eyes the eyes will be captured, whether it is closed or open. If the eyes are found closed for 4 consecutive frames, it is confirm that the driver is in drowsiness condition.

Keywords: Open CV, Tensor Flow, Detection, Drowsiness System, Machine Learning system


Paper Id: 230596

Published On: 2024-04-23

Published In: Volume 12, Issue 2, March-April 2024

Cite This: IOT Based Driver Drowsiness Detection System - Kalpesh Shinde, Lalit Borse, Pankaj Khaire, Triveni Pise, Prof. Naresh Shende - IJIRMPS Volume 12, Issue 2, March-April 2024.

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