Driver Drowsiness and Distraction Detection using Machine Learning

Abstract

Nowadays, a countless number of people tend to use different types of transportation for travelling from one place to another either through land, air or water. It is very dangerous to drive when you feel asleep. Long-distance drivers suffer from insomnia. The drivers should be very cautious while driving and even he/she have to be very careful while driving at night time. Driver's drowsiness is the real cause of many road accidents. Therefore, there is a necessity for developing a machine learning model that will detect and notify a driver’s bad psychophysical condition and unnecessary distraction which could significantly reduce the number of road accidents. These factors lead to the innovation of this project in order to support the drivers for their drowsiness condition and prevent their distraction. This project uses the technology of OpenCV (Computer Vision) powered by Machine Learning in order to detect the eyes, head and mouth movements while driving. However, the development of such systems encounters many difficulties associated with the fast and accurate recognition of the driver's drowsiness symptoms. One of the best ways to detect a drowsy driver is to use a vision-based approach.