Drowsy Driver Detection using Eye-Tracking through Machine Learning

Eye tracking is one of the most useful but underutilized technologies in today's world. It can be used in a variety of ways now that the technology is available. We propose an implementation that has not been done but should be after studying several ways to process the same. In the field of Ad...

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Bibliographic Details
Published in2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) pp. 1916 - 1923
Main Authors S, Akshay, MB, Abhishek, D, Sudhanshu, C, Anuvaishnav
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.08.2021
Subjects
Adaptive Boosting
Cameras
Communication systems
DLib
Driver Assistance
Driver Drowsiness
Driver Monitoring System
Eye Tracking
Feeds
Gaze tracking
Haar Cascade Classifier
HOG
Machine learning
Organizations
PERCLOS
Road accidents
SVM
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Summary:Eye tracking is one of the most useful but underutilized technologies in today's world. It can be used in a variety of ways now that the technology is available. We propose an implementation that has not been done but should be after studying several ways to process the same. In the field of Advanced Driving Assistance Systems, tracking drivers' eyes is a hot topic (ADAS). According to data from the World Health Organization (WHO), approximately 1-1.25 million people die and 20-50 million people suffer from non-fatal injuries in road accidents each year around the world. And a high majority of these collisions are caused by drowsy driving. Our paper explores a possible implementation that could help detect drowsiness given a subject, a phone camera and a single board computer. We establish a connection between the phone and the system using a network connection that streams the camera feed onto the system, which further performs computations to determine the drowsiness of the driver.
DOI:10.1109/ICESC51422.2021.9532928