Realtime Driver Drowsiness Detection Using Machine Learning
Author:
Affiliation:
1. American University of Ras Al Khaimah,Department of Computer Science and Engineering,Ras Al Khaimah,UAE
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9734305/9734306/09734801.pdf?arnumber=9734801
Reference31 articles.
1. Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework
2. Deep CNN models-based ensemble approach to driver drowsiness detection
3. Real time drowsiness detection using eye blink monitoring
4. Real-time eye blink detection using facial landmarks;soukupova;21st Comput Vis Winter Work,2016
5. Detecting Driver Drowsiness Based on Sensors: A Review;arun;MDPI Sensors,0
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2. Driver Drowsiness Detection System Based on Yawn Detection;Algorithms for Intelligent Systems;2024
3. Smart Road Safety: An IoT Approach to Driver Drowsiness Detection and Prevention;Convergence of Machine Learning and IoT for Enabling the Future of Intelligent Systems;2024
4. Driving Alert Application Based On Facial Expression Using Landmark Algorithm;2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE);2023-12-02
5. Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features;Journal of Imaging;2023-04-29
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