Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform
DOI:
https://doi.org/10.14464/ess.v8i2.491Abstract
the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones

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Copyright (c) 2021 Julkar Nine, Naeem Ahmed, Rahul Mathavan

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Copyright for articles published in this journal is retained by the authors. The content is published under a Creative Commons Licence Attribution 4.0 International (CC BY 4.0). This permits use, distribution, and reproduction in any medium, provided the original work is properly cited, and is otherwise in compliance with the licence.