Detection of Driver Drowsiness Based on Eye and Mouth Movements Using Convolutional Neural Networks-Reference-Cited by-同舟云学术

Detection of Driver Drowsiness Based on Eye and Mouth Movements Using Convolutional Neural Networks

Published:2022-11-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications
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Author:

Kusumo Budiarianto Suyo¹^ORCID,Oktaviana Siwi²^ORCID,Sulandari Winita³^ORCID,Heryana Ana¹^ORCID,Yuwana R Sandra¹^ORCID,Suryawati Endang¹^ORCID,Yuliani Asri Rizki¹^ORCID,Pardede Hilman¹^ORCID

Affiliation:

1. National Research and Innovation Agency (BRIN), Indonesia

2. University of Sebelas Maret, Indonesia

3. Universitas Sebelas Maret, Indonesia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3575882.3575909

Reference31 articles.

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3. Brian C. Tefft . 2014. Prevalence of Motor Vehicle Crashes Involving Drowsy Drivers , United States , 2009 -2013 (Technical Report) . Washington, D.C. : AAA Foundation for Traffic Safety . Brian C. Tefft. 2014. Prevalence of Motor Vehicle Crashes Involving Drowsy Drivers, United States, 2009-2013 (Technical Report). Washington, D.C.: AAA Foundation for Traffic Safety.

4. Prevalence of motor vehicle crashes involving drowsy drivers, United States, 1999–2008

5. Camera-based drowsiness reference for driver state classification under real driving conditions