A System on Intelligent Driver Drowsiness Detection Method

Kumar, Ugra Mohan; Singh, Devendra P.; Jugran, Sudhir; Punia, Pankaj; Negi, Vinay

doi:10.14419/ijet.v7i3.4.16765

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…When determining eye closure-associated indicators, most researchers considered only eye closures that lasted no less than 150 ms to 250 ms to distinguish them from ordinary eye blinks [47,[53][54][55][56][57][58][59][60][61]. Nevertheless, eye blinks are also considered in the latest literature on driver fatigue research [28,[62][63][64], as is eye tracking [65,66]. Additional information is obtained by tracking the position of the driver's head.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Dziuda

Baran

Zieliński

et al. 2021

Sensors

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This paper presents a camera-based prototype sensor for detecting fatigue and drowsiness in drivers, which are common causes of road accidents. The evaluation of the detector operation involved eight professional truck drivers, who drove the truck simulator twice—i.e., when they were rested and drowsy. The Fatigue Symptoms Scales (FSS) questionnaire was used to assess subjectively perceived levels of fatigue, whereas the percentage of eye closure time (PERCLOS), eye closure duration (ECD), and frequency of eye closure (FEC) were selected as eye closure-associated fatigue indicators, determined from the images of drivers’ faces captured by the sensor. Three alternative models for subjective fatigue were used to analyse the relationship between the raw score of the FSS questionnaire, and the eye closure-associated indicators were estimated. The results revealed that, in relation to the subjective assessment of fatigue, PERCLOS is a significant predictor of the changes observed in individual subjects during the performance of tasks, while ECD reflects the individual differences in subjective fatigue occurred both between drivers and in individual drivers between the ‘rested’ and ‘drowsy’ experimental conditions well. No relationship between the FEC index and the FSS state scale was found.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Dziuda

Baran

Zieliński

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In their system, an SVM was employed to extract visual features, such as eye and mouth aspect ratios, blink rate, and yawning rate. Kumar et al [ 26 ] utilized a camera and Harr’s feature classifier to extract the eye area for driver drowsiness detection.…”

Section: Related Workmentioning

confidence: 99%

“…Eye and mouth features are the most frequently used features for remote camera-based drowsiness detections [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. In this type of system, face landmarks are extracted as the first step to identify eye and mouth features.…”

Section: Related Workmentioning

confidence: 99%

A CNN-Based Wearable System for Driver Drowsiness Detection

Zhang

Zhu

et al. 2023

Sensors

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Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.

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Drowsiness Detection System Using Deep Learning Methods

Valsson¹,

Mahalle²,

Baisware³

et al. 2022

IJARSCT

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Drowsiness and driver fatigue cause a great percentage of road accidents. The number of deaths and fatalities injuries due to sleeplessness is increasing every year globally. The drowsy state, caused due to no breaks during a long-distance journey can be detected with an attention assist system, a method that can warn inactiveness and warn the driver about the current state of fatigue by detecting eyes as the region of interest. Our system will locate, track and analyze the driver’s eyes and create an alert for the navigation system. The monitoring system is a real-time system that will use machine learning and computer vision to detect driver fatigue and distraction and efficiently avoid and reduce road-accidents caused. The model is able to predict the drowsiness of driver. It is able to analyze whether the eyes are open or closed and face not detected and displayed the intended output with 99.8% accuracy.

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A System on Intelligent Driver Drowsiness Detection Method

Cited by 3 publications

References 20 publications

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

A CNN-Based Wearable System for Driver Drowsiness Detection

Drowsiness Detection System Using Deep Learning Methods

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