fatigue state detection for tired persons in presence of driving periods
Priya.v,Deepika.k,Muzeebha.m,Sumithra.k.s
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 23 April,2024 Pages:1936-1942
Abstract
Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this dangerous condition. Several datasets have been used in the development of fatigue or drowsy detection techniques. These images recognize live motion of action behavior in our dataset. And the evaluated data are trained using Machine learning techniques mysterious data estimated using Deep learning techniques. The machine learning approach is used to process Image dataset, whereas the deep learning approach is used to process video streams. In deep learning models, VGG16 architecture, provides up to 98% detection accuracy, which is the highest accuracy among the deployed models.
Kewords
Machine learning, Convolutional Neural Network Algorithm
Reference
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