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2021 | OriginalPaper | Chapter

SVM-Based Drivers Drowsiness Detection Using Machine Learning and Image Processing Techniques

Authors : P. Rasna, M. B. Smithamol

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

In this paper, we propose an efficient algorithm for driver drowsiness detection and efficient alert system. The existing works mainly follow vehicle-based measures, physiological-based measures, behavioral-based measures. Moreover, the works based on behavioral measures mainly focused on eye movements, yawning, and head position. The proposed method uses more relevant and appropriate behavioral features such as significant variation in aspect ratio of eyes, mouth opening ratio, nose length bending, and the changes that happened in eyebrows, wrinkles, ear due to drowsiness. The binary SVM classifier is used for classification whether the driver is drowsy or not. The inclusion of these features helped in developing more efficient driver drowsiness detection system. The proposed system shows 97.5% accuracy and 97.8% detection rate.

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Metadata
Title
SVM-Based Drivers Drowsiness Detection Using Machine Learning and Image Processing Techniques
Authors
P. Rasna
M. B. Smithamol
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-6353-9_10