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A Systematic Review of Driver Drowsiness Detection using Various Approaches.

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Τίτλος: A Systematic Review of Driver Drowsiness Detection using Various Approaches.
Συγγραφείς: Ashture, Sneha N., Mane, Sunil B.
Πηγή: Grenze International Journal of Engineering & Technology (GIJET); Jun2022, Vol. 8 Issue 2, p811-817, 7p
Περίληψη: Drowsiness is one of the leading causes of road accidents, hence a monitoring system is required to identify drowsiness. Driver monitoring systems typically detect three sorts of data: biometric, vehicle, and driver graphic. Nowadays, several devices including navigation systems and warning alarm systems are available to help drivers. The human mistake causes numerous traffic fatalities and injuries worldwide. Drowsiness and mapping while driving is widely recognized as contributing factors to deadly car accidents. This article reviews several sleepiness detecting methods. The characteristics of these approaches are categorized and contrasted. One of them is computer vision-based picture processing. It utilizes the driver's eyes and facial gestures to identify tiredness. This survey study focuses on this strategy. [ABSTRACT FROM AUTHOR]
Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Grenze International Journal of Engineering & Technology (GIJET); Jun2022, Vol. 8 Issue 2, p811-817, 7p
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  Data: Drowsiness is one of the leading causes of road accidents, hence a monitoring system is required to identify drowsiness. Driver monitoring systems typically detect three sorts of data: biometric, vehicle, and driver graphic. Nowadays, several devices including navigation systems and warning alarm systems are available to help drivers. The human mistake causes numerous traffic fatalities and injuries worldwide. Drowsiness and mapping while driving is widely recognized as contributing factors to deadly car accidents. This article reviews several sleepiness detecting methods. The characteristics of these approaches are categorized and contrasted. One of them is computer vision-based picture processing. It utilizes the driver's eyes and facial gestures to identify tiredness. This survey study focuses on this strategy. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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