Retracted: Deep Learning based Drowsiness Detection and Monitoring using Behavioural Approach

P. William · Ajay Reddy Yeruva · Durgaprasad Gangodkar · Swati Vashisht · Amarendranath Choudhury

45 citations0/0 files storedInternational Conference on Technological Advancements in Computational Sciences (ICTACS)

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Sleep and Work-Related Fatigue Human-Automation Interaction and Safety IoT and GPS-based Vehicle Safety Systems

Abstract

Using deep learning and a behavioural approach, this study presents a real-time detection and monitoring system for tired drivers. The objective is to develop and build software that collects real-time driver behaviour while driving and trains it using convolutional neural networks (CNNs) to anticipate the driver's behaviour. An intelligent video-based gadget, a dataset of drowsy drivers, and CNN architecture were used to achieve this goal. MATLAB and a deep learning technology were used to implement the concepts. Tests revealed that the system has a 99.8% accuracy rate for detecting anomalies. A prototype model of the system was created using MATLAB.

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2010Drowsy driver detection system using eye blink patterns 2017Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks 2019A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration 2018Driver drowsiness monitoring system using visual behaviour and machine learning 2017Driver drowsiness detection using facial dynamic fusion information and a DBN 2018Early Detection of Driver Drowsiness Utilizing Machine Learning based on Physiological Signals, Behavioral Measures, and Driving Performance 2010Drowsiness Monitoring By Steering And Lane Data Based Features Under Real Driving Conditions 2019Smart Real-Time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure

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