Driver fatigue detection system

Clément, Frédérique; Vashistha, Aditya; Rane, Milind

doi:10.1109/infop.2015.7489384

Cited by 15 publications

(8 citation statements)

References 2 publications

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“…Clement et al [34] developed a fatigue detection system using output of different low pass and band pass filters on Electro-OculoGram (EOG) and Electro-Encephalogram (EEG) signals. They did not report their results in terms of alarm generation accuracy.…”

Section: Khan and Mansoormentioning

confidence: 99%

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Naz¹,

Ziauddin²,

Raza³

2019

IJIMAI

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Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes.

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Section: Khan and Mansoormentioning

confidence: 99%

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Naz¹,

Ziauddin²,

Raza³

2019

IJIMAI

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“…Las investigaciones en este campo enfocan cuatro categorías para la detección de la fatiga. La primera consiste en la señal fisiológica de los conductores, entre ellas electroencefalograma (EEG), electrocardiógrafo (ECG) y electrooculograma (EOG) [18,16]. Esta categoría da buenos resultados, pero obtener estas señales suele ser muy complicado y trabajoso.…”

Section: Introductionunclassified

Detección del estado fisiológico de los ojos en Conductores mediante técnicas de visión artificial

Ale

Fabian

2019

Ingeniare. Rev. chil. ing.

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En las últimas décadas, la cantidad de accidentes de tránsito debido a la fatiga o somnolencia del conductor ha provocado cuantiosas pérdidas humanas y materiales. A su vez, la venta en el parque automotor se ha masificado lo cual indica que posiblemente en los siguientes años si no se toma las medidas pertinentes para detectar la fatiga existirá un incremento en los accidentes automovilísticos. Este trabajo de investigación propone el desarrollo de un sistema de detección de fatiga en los conductores que permita alertar sobre sobre su estado mientras esté conduciendo mediante el uso de técnicas de visión artificial y machine learning. Las técnicas de estos dos campos de estudio se interceptan para generar modelos supervisados con un alto rendimiento al momento de clasificar el estado de fatiga en los conductores. En este estudio se ha trabajado con un dataset de imágenes frontales enfocándonos en la característica fisiológica de los ojos obteniendo resultados preliminares prometedores en la detección de fatiga en tiempo real.

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“…For example, some of them focused on the detection of the driver lane changing intention, 6 speed/acceleration intention, 10,11 and/or brake/accelerate intention. 8,12 Some developed DBMs focused on the prediction of some physiological or mental states such as driver vigilance, 7,13,14 fatigue, [15][16][17][18] drowsiness, [19][20][21][22] aggressiveness, 23 or driver motivation. 5 Developed DBMs consider different factors that can affect the driver behavior.…”

Section: Introductionmentioning

confidence: 99%

A new framework for the computer modelling and simulation of car driver behavior

Elkosantini

Darmoul

2018

SIMULATION

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In recent years, the simulation of personal car driver behavior has attracted increasing attention in recent research works. Such works are based on models and systems derived from social and psychological studies. The complexity of the simulation of such systems is due to the need for modeling driver behavior and the integration of psychological and physiological factors that can affect driver performance. Although there is only a limited number of models that have been proposed to simulate driver behavior, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed in this paper. This investigation work focuses on the development of a new model for driver behavior simulation based on recent physiological and psychological theories. The model aims to reproduce the driver behavior with respect to some psychological factors. An experimental framework is also presented to build the simulation model. This article concludes by describing some examples of use or application of the suggested model.

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Driver fatigue detection system

Cited by 15 publications

References 2 publications

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Detección del estado fisiológico de los ojos en Conductores mediante técnicas de visión artificial

A new framework for the computer modelling and simulation of car driver behavior

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