Open AccessProceedings Article
Driver drowsiness detection using ANN image processing
Tiberiu Vesselenyi,Sorin Moca,Alexandru Rus,Tudor Mitran,Bogdan Tataru +4 more
- 21 Aug 2017
34
TL;DR: In this paper, the authors presented a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis.
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Abstract: The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver’s eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.
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Citations
A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver
TL;DR: Different types of measures used in sleepiness detection systems (SDSs) are reviewed and several techniques proposed in ESDSs to optimize the number of EEG electrodes, increasing the sleepiness level resolution and incorporation of circadian information are discussed.
135
Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection.
TL;DR: This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor, and offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.
85
An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal
Francesco Rundo,Sergio Rinella,Simona Massimino,Marinella Coco,Giorgio Fallica,Rosalba Parenti,Sabrina Conoci,Vincenzo Perciavalle +7 more
- 28 Feb 2019
TL;DR: Results obtained indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices and can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical.
65
Driver Drowsiness Detection Based on Joint Monitoring of Yawning, Blinking and Nodding
Aicha Ghourabi,Haythem Ghazouani,Walid Barhoumi +2 more
- 03 Sep 2020
TL;DR: A reliable method towards drowsiness detection by analyzing images of the driver’s face by combining eye closure and yawning by measuring the eye and the mouth aspect ratios, and head pose which is estimated by analyzing the optical flow is proposed.
42
Driver Drowsiness Detection using Deep Learning
Yeresime Suresh,Rashi Khandelwal,Matam Nikitha,Mohammed Fayaz,Vinaya Soudhri +4 more
- 07 Oct 2021
TL;DR: In this paper, a sleepiness detection system that detects whether a driver’s eyes are closed for a few seconds, and then alerts the driver via an alarm is presented.
16
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