Monitoring Driver’s Drowsiness Status at Night Based on Computer Vision

A, Vidhu Valsan; Mathai, Paul P; Babu, Ierin

doi:10.1109/icccis51004.2021.9397180

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…It cuts down on processing time considerably. According to Valsan, Mathai, and Babu [20], the central feature of Driver Sleepiness Detection is obtaining an image of the driver's face, analyzing the image obtained, and estimating the drowsiness level, SVM (Support Vector Machine) is a sort of machine learning technique that employs. To achieve this, we need proper hardware.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Boppuru,

Kukatlapalli,

Kukatlapalli

et al. 2023

JAMRIS

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The number of vehicles on the road rises in tandem with the development of vehicle manufacture. With the rapid growth of automobiles, the number of road accidents appears to be steadily increasing. Accidents are a typical occurrence in our daily lives. Road accidents are the top cause of death from injuries worldwide in the top 10. It now forms an amazingly important part of the global burden of illness. An estimated 1.2 million people are killed in accidents every year, and an estimated 50 million are injured while receiving bone marrow transplants in leading countries. Drowsiness, along with fatigue of drivers, are some of the major causes of road accidents. Every year, we see an increase in death numbers worldwide. This study also looks into automatically detecting driver drowsiness with AI, visuals, and CNN. The Driver Drowsiness System works on the concept of Multi-Layer Feed Forward Network, especially on Convolutional Neural Networks (CNN). CNN was developed with the help of around 7000 images of eyes in both drowsiness and non-drowsiness states with different facial architectures. These images were split into training (80 percent of images) and testing (20 percent of images) datasets. The images under the training dataset are given as the input for the network for training purposes. Backpropagation algorithms and optimizers are used to reduce the loss as much as possible. We created an algorithm for detecting, tracking, and analysing motor and visual effects to measure ROI.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Boppuru,

Kukatlapalli,

Kukatlapalli

et al. 2023

JAMRIS

View full text Add to dashboard Cite

show abstract

“…Usually, mouth features are combined with eye features to ensure drivers' psychophysiological states. Mouth features include yawning frequency, mouth opening ratio, mouth state, mouth aspect ratio (MAR), and Open Mouth Rate (OMR) Liu et al 2020;Rathi et al, 2021;Valsan et al 2021;Wang and Qu 2021;Wei et al 2021;Zhao et al 2021;Zhongwei et al 2021;Zhuang and Qi 2021).…”

Section: Literature Review Of Drivers' Psychophysiological State Dete...mentioning

confidence: 99%

“…Also, it is not affected by the environmental aspects or face occlusion such as brightness or wearing glasses. Valsan et al (2021) had used HBR, foot galvanic skin response (FGSR), and hand galvanic skin response (HGSR) to extract psychophysiological features, i.e., stress. Those measures proved to be beneficial and convenient for stress detection as the signals can be obtained through a wearable device.…”

Section: Literature Review Of Drivers' Psychophysiological State Dete...mentioning

confidence: 99%

“…It has a wide range of usage especially in face and image recognition, speech recognition and text categorization. In addition, the method has proven significance in validating, detecting and classifying facial features (Karamizadeh et al 2014;Travieso-González et al 2021;Valsan et al 2021;Zhongwei et al 2021). Moreover, it was used to detect diver's psychophysiological state by classifying EEG (Ahmadi et al 2021) and sEMG (Lu et al 2021).…”

Section: Analysis Of Drivers' Psychophysiological State Detection Mea...mentioning

confidence: 99%

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Development of Drivers’ Psychophysiological State Detection Measures and Relevant Machine Learning Methods

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Driver impairment is yet another human factor that is likely to cause dangerous driving behavior (aka distracted driving), leading to safety hazards on the roads. Assuring acceptable drivers' cognitive state can improve safe driving performance. Behavioral and self-report measures are used to assess drivers' cognitive states, but psychophysiological measures can add more considerable value in the real-world driving context. Hence, monitoring, detecting, and responding to drivers' psychophysiological state compose a safety countermeasure that can reduce the risk of distracted

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Artificial Intelligence Systems in the Field of Safety at Work and Life

Zhmud

2023

Studies in Systems, Decision and Control

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Monitoring Driver’s Drowsiness Status at Night Based on Computer Vision

Cited by 11 publications

References 6 publications

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Development of Drivers’ Psychophysiological State Detection Measures and Relevant Machine Learning Methods

Artificial Intelligence Systems in the Field of Safety at Work and Life

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