Driver drowsiness monitoring system using machine learning (SVM algorithm)-Reference-Cited by-同舟云学术

Driver drowsiness monitoring system using machine learning (SVM algorithm)

Published:2023 Issue: Volume: Page:
ISSN:0094-243X
Container-title:PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY
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Author:

Kumar J. M. S. V. Ravi,Kumar M. N. Naveen,Narendra N. V.,Pradeep P.,Varma M. R. Krishna

Publisher

AIP Publishing

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0113564

Reference15 articles.

1. Speech P. Smith, M. Shah, and N.V. Lobo, “Monitoring head/eye motion for driver alertness with one camera” Proceedings. 15th International Conference on Pattern Recognition (Volume: 4) September 2000.

2. Claudio A , “Face and eye tracking algorithm based on digital image processing”, Department of Electrical Engineering, Universidad de Chile AV. Tupper 2007..

3. Pınar Boyra, John H. L. Hansen, “Active Accident-Avoidance Case Study: Integrating Drowsiness Monitoring System with Lateral Control and Speed Regulation in Passenger Vehicles” >Proceedings of the 2008 IEEE International Conference on Vehicular Electronics and Safety Columbus, OH, USA. September 22-24, 2008.

4. An evolutionary algorithm that constructs recurrent neural networks

5. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation

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