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Yawn based driver fatigue level prediction

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conference contribution
posted on 2020-01-01, 00:00 authored by H A Kassem, Morshed ChowdhuryMorshed Chowdhury, Jemal AbawajyJemal Abawajy, Ahmed Raad Abdul Hussein Al-Sudani
The fatigue-related accident is increasing due to long work hours, medical reasons, and age that decrease response time in a moment of hazard. One of drowsiness and fatigue visual indicators is excessive yawning. In this paper, a non-optical sensor presented as a car dashcam that is used to record driving scenarios and imitates real-life driving situations such as being distracted or talking to a passenger next to the driver. We built a deep CNN model as the classifier to classify each frame as a yawning or non- yawning driver. We can classify the drivers' fatigue into three levels, alert, early fatigue and fatigue based on the judgement of the number of yawns. Alert level means when the driver is not yawning, while, early fatigue is when the driver yawns once in a minute. Fatigued is when the driver yawns more than once in a minute. An overall decision is made by analyzing the source score and the condition of the driver's fatigue state. The robustness of the proposed method was tested under various illumination contexts and a variety of head motion modes. Experiments are conducted using YAWDD dataset that contains 322 subjects to show that our model presents a promising framework to accurately detect drowsiness level in a less complex way.

History

Event

Computers and Their Applications. Conference (2020 : 35th : Online)

Series

EPiC Series in Computing; v.69

Pagination

372 - 382

Publisher

ISCA

Location

Online

Place of publication

[unknown]

Start date

2020-03-09

End date

2020-03-09

eISSN

2398-7340

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

CATA 2020 : Proceedings of 35th International Conference on Computers and Their Applications

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