Deep CNN models-based ensemble approach to driver drowsiness detection

Statistics have shown that many accidents occur due to drowsy condition of drivers. In a study conducted by National Sleep Foundation, it has been found that about 20% of drivers feel drowsy during driving. These statistics paint a very scary picture. This paper proposes a system for driver drowsine...

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Veröffentlicht in:	Neural computing & applications Jg. 33; H. 8; S. 3155 - 3168
Hauptverfasser:	Dua, Mohit, Shakshi, Singla, Ritu, Raj, Saumya, Jangra, Arti
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2021 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Blinking Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Driver fatigue Feature extraction Head movement Image Processing and Computer Vision Indoor environments Machine learning Original Article Probability and Statistics in Computer Science Yawning Deep learning AlexNet CNN FlowImageNet VGG-FaceNet ResNet
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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