Advanced Driver Fatigue Detection by Integration of OpenCV DNN Module and Deep Learning-Reference-Cited by-同舟云学术

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Advanced Driver Fatigue Detection by Integration of OpenCV DNN Module and Deep Learning

Published:2023-03-13 Issue: Volume: Page:
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Author:

Parvez M. Muzammil¹^ORCID,Allanki Srinivas¹^ORCID,Sudhagar Govindaswamy²,R. S. Ernest Ravindran¹^ORCID,Santosh Chella¹,Mohammed Ali Baig³^ORCID,Muqeet Mohd. Abdul⁴

Affiliation:

1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522502, India

2. Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai 600126, India

3. School of Electronics and Communication Engineering, Reva University, Bangaluru 560064, India

4. Department of Electrical Engineering, Muffakham Jah College of Engineering and Technology, Hyderabad 500034, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/34/1/15/pdf

Reference20 articles.

1. Drowsiness Detection System;Dhawde;Int. J. Eng. Res. Technol. (IJERT) Iconect,2015

2. A critical review of the psychophysiology of driver fatigue;Lal;Biol. Psychol.,2001

3. A Survey on State-of-the-Art Drowsiness Detection Techniques;Ramzan;IEEE Access,2019

4. Detecting Driver Drowsiness Based on Sensors: A Review;Sahayadhas;Sensors,2012

5. Sun, Y., Chen, Y., Wang, X., and Tang, X. (2014). Deep learning face representation by joint identification-verification. Adv. Neural Inf. Process. Syst.

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