Deep CNN models-based ensemble approach to driver drowsiness detection

• Published in: Neural computing & applications Vol. 33; no. 8; pp. 3155 - 3168
• Main Authors: Dua, Mohit, Shakshi, Singla, Ritu, Raj, Saumya, Jangra, Arti
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2021; Springer Nature B.V
• Subjects: 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

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...