Zobrazeno 1 - 10
of 9 020
pro vyhledávání: '"drowsiness detection"'
Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that combines Electroencephalography (EE
Externí odkaz:
http://arxiv.org/abs/2408.16145
Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection plays a critical role in preven
Externí odkaz:
http://arxiv.org/abs/2408.12990
Drowsiness detection is essential for improving safety in areas such as transportation and workplace health. This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facial landmark detection technique
Externí odkaz:
http://arxiv.org/abs/2408.05836
Autor:
Zayed, Aymen1,2,3 (AUTHOR) aymen.zayed@umons.ac.be, Belhadj, Nidhameddine4 (AUTHOR) nidhameddine.belhadj@fsm.rnu.tn, Ben Khalifa, Khaled1,5 (AUTHOR) khaled.benkhalifa@issatso.rnu.tn, Bedoui, Mohamed Hedi1 (AUTHOR) medhedi.bedoui@fmm.rnu.tn, Valderrama, Carlos3 (AUTHOR) carlos.valderrama@umons.ac.be
Publikováno v:
Sensors (14248220). Jul2024, Vol. 24 Issue 13, p4256. 33p.
Ensuring driver readiness poses challenges, yet driver monitoring systems can assist in determining the driver's state. By observing visual cues, such systems recognize various behaviors and associate them with specific conditions. For instance, yawn
Externí odkaz:
http://arxiv.org/abs/2405.03311
Autor:
Quiles-Cucarella, Eduardo1 (AUTHOR) equiles@isa.upv.es, Cano-Bernet, Julio1 (AUTHOR) lusanfe1@etsid.upv.es, Santos-Fernández, Lucas1 (AUTHOR), Roldán-Blay, Carlos2 (AUTHOR) carrolbl@die.upv.es, Roldán-Porta, Carlos2 (AUTHOR) croldan@die.upv.es
Publikováno v:
Sensors (14248220). Sep2024, Vol. 24 Issue 17, p5683. 35p.
This study presents a novel driver drowsiness detection system that combines deep learning techniques with the OpenCV framework. The system utilises facial landmarks extracted from the driver's face as input to Convolutional Neural Networks trained t
Externí odkaz:
http://arxiv.org/abs/2406.15646
Publikováno v:
Journal of Applied Science and Engineering, Vol 27, Iss 7, Pp 2869-2881 (2024)
Driver drowsiness can cause serious harm to drivers and other road participants. Exploring objective and efficient methods for detecting driver drowsiness has important application value for ensuring road safety. Considering the information complemen
Externí odkaz:
https://doaj.org/article/f83cc343c7de4976a9684ffceaeb7d0a
Autor:
Dina Salem, Mohamed Waleed
Publikováno v:
Journal of Engineering and Applied Science, Vol 71, Iss 1, Pp 1-15 (2024)
Abstract Drowsiness detection is a critical aspect of ensuring safety in various domains, including transportation, online learning, and multimedia consumption. This research paper presents a comprehensive investigation into drowsiness detection meth
Externí odkaz:
https://doaj.org/article/21e8993d7a56473b850d808d9d9c6990
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