Paper Contents
Abstract
The number of traffic accidents caused by drowsy drivers is increasing at an alarming rate. If you've ever driven, you've been drowsy behind the wheel at some point. It's not something we like to admit, but it's an important issue with serious consequences that needs to be addressed. The scariest part is that drowsy driving doesn't just mean falling asleep while driving. Drowsy driving can be as minor as a brief state of inattention where the driver does not pay full attention to the road. Automated contactless system that he can recognize the drowsiness of the driver in time is needed for hours. Our project describes a machine learning approach for sleepiness detection. Face detection is used to locate the driver's eye regions, which are used as templates for eye tracking in subsequent images. Finally, images of the tracked eye are used for sleepiness detection to generate warning alarms. This proposed approach has three phases: face detection, eye detection and sleepiness detection. Our project describes a machine learning approach for sleepiness detection. Face detection is used to locate the driver's eye regions, which are used as templates for eye tracking in subsequent images. Finally, they are images of the tracked eye used to detect sleepiness to generate warning alarms.
Copyright
Copyright © 2024 Kundan Aher. This is an open access article distributed under the Creative Commons Attribution License.
