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10.1016_j.procs.2018.04.060.pdf (1 MB)

Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

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submitted on 2024-07-09, 11:48 and posted on 2024-07-09, 11:48 authored by Rateb Jabbar, Khalifa Al-Khalifa, Mohamed Kharbeche, Wael Alhajyaseen, Mohsen Jafari, Shan Jiang

Road crashes and related forms of accidents are a common cause of injury and death among the human population. According to 2015 data from the World Health Organization, road traffic injuries resulted in approximately 1.25 million deaths worldwide, i.e. approximately every 25 seconds an individual will experience a fatal crash. While the cost of traffic accidents in Europe is estimated at around 160 billion Euros, driver drowsiness accounts for approximately 100,000 accidents per year in the United States alone as reported by The American National Highway Traffic Safety Administration (NHTSA). In this paper, a novel approach towards real-time drowsiness detection is proposed. This approach is based on a deep learning method that can be implemented on Android applications with high accuracy. The main contribution of this work is the compression of heavy baseline model to a lightweight model. Moreover, minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy. The proposed model is able to achieve an accuracy of more than 80%.

Other Information

Published in: Procedia Computer Science
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.procs.2018.04.060

Funding

Modeling and Simulation of Road Safety and Traffic System in the State of Qatar

Qatar National Research Fund

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Qatar National Research Fund (NPRP8-910-2-387), Modeling and Simulation of Road Safety and Traffic System in the State of Qatar.

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2018

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • Qatar Transportation and Traffic Safety Center - CENG

Geographic coverage

Qatar