A Novel Design approach for Driver Drowsiness detection system
Abstract
Major traffic collisions are happening more frequently, and most of them are considered to be the driver's fault. Approximately 78 percent of accidents are attributed to drivers, according to a 2015 poll. A monitoring device that informs the driver when he nods off is suggested to reduce the likelihood of these events. This program analyzes a live video feed of the driver's face while monitoring his mouth and eye movements to gauge how often he yawns and closes his eyes. If the driver is sleepy or already sleeping, an alarm rings. To identify eye closure and yawning, Haar-cascade classifiers run concurrently on the collected face data. Even the project deals with driver’s behavior by using Facial Landmarks to detect driver’s suspicious behavior.
Keywords
Drowsiness, Driver, AI