Real Time Fatigue Detection Using Shape Predictor 68 Face Landmarks Algorithm

Abstract

One of the most important challenges confronting the world today is the rise in road accidents. Improper and inattentive driving is the leading cause of road accidents. This study’s main goal is to develop a non-intrusive system that can detect human fatigue and provide an early warning. Drivers who do not stop frequently when driving long distances are at risk of becoming drowsy, which they sometimes do not realise until it is too late. The driver’s drowsiness or lack of concentration is regarded to be a primary factor in such incidents. Driver sleepiness monitoring research could aid in the reduction of accidents. According to expert research, about a quarter of serious highway accidents can be attributed to sleepy drivers who need to rest, which means that sleepy drivers cause more traffic accidents than drink-driving. The technology will employ a camera to follow and monitor drivers’ eyes, and by building a Landmarks algorithm, we will be able to detect sleepiness symptoms in drivers early enough to avoid accidents. As a result, this research will assist in detecting a driver’s tiredness in advance and providing warning output in the form of alarms and pop-up windows. Furthermore, rather than being disabled automatically, the warning will be disabled manually. This will identify tiredness or fatigue and can be used to automatically slow the vehicle down.