CNN-Based Driver Drowsiness Detection System-Reference-Cited by-同舟云学术

CNN-Based Driver Drowsiness Detection System

Published:2021 Issue: Volume: Page:153-166
ISSN:2522-8595
Container-title:EAI/Springer Innovations in Communication and Computing
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Author:

Singh Pritesh Kumar,Upadhyay Mayank,Gupta Archit,Lamba Puneet Singh

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-76167-7_10

Reference35 articles.

1. S. Sangle, B. Rathore, R. Rathod, A. Yadav, and A. Yadav, “Real Time Drowsiness Detection System,” pp. 87–92, 2018. (Raspberry Pi).

2. K. Das and R. N. Behera, “A survey on machine learning: Concept, algorithms and applications,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, no. 2, pp. 1301–1309, 2017.

3. Alioua, N., Amine, A., Rziza, M. and Aboutajdine, D., 2020. Driver’s fatigue and drowsiness detection to reduce traffic accidents on road.

4. Mehta, S., Dadhich, S., Gumber, S. and Jadhav Bhatt, A., 2020. Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio.

5. T. Soukupova and J. Cech, “Real-time eye blink detection using facial landmarks,” Computer Vision Winter Workshop (CVWW), 2016.

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