Driver Drowsiness Detection using Representation Learning and Efficient Alarming System
Keywords:
Eye Segmentation, Drowsiness Detection,Alarming system,Representation Learning, Facial Features.Abstract
Computing technology has provided assistance to drivers mainly in the form of intelligent vehicle systems..
Thus, driver drowsiness detection has been considered a major huge number of sleep induced road accidents. Driver
fatigue is a significant factor in a large number of vehicle accidents The main aim of the project is to develop Drowsy
Driver Detection System that allows for warning the driver of drowsiness or in attention to prevent traffic accidents. We
propose a vision based intelligent algorithm to detect driver drowsiness. Previous approaches are generally based on
blink eye, eye close rate, eye brow shape and other hand engineered facial expressions. Our system proposes an
algorithm for driver drowsiness detection using representation learning and Efficient Alarming System. A The proposed
algorithm makes use of features learnt using neural network so as to explicitly capture various latent facial features and
the complex non-linear feature interactions. A softmax layer is used to classify the driver as drowsyness or nondrowsyness. This system is hence used for warning the driver of drowsiness or in attention to prevent traffic accidents.
We present both qualitative and quantitative result.
