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Detecting Fatigue Driving Through PERCLOS: A Review
Samuel Kim, Irfan Wisanggeni, Ryan Ros, Rania Hussein
Pages - 1 - 7     |    Revised - 31-01-2020     |    Published - 29-02-2020
Volume - 14   Issue - 1    |    Publication Date - February 29, 2020  Table of Contents
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KEYWORDS
PERCLOS, Real-time Systems, Autonomous Driving.
ABSTRACT
In this paper, we present a literature survey about drowsy driving detection using PERCLOS metric that determines the percentage of eye closure. This metric determines that an eye is closed if the percentage of eye closure is 80% or above. When this percentage is observed for multiple frames of a video camera feed, the driver is determined to be in an unsafe fatigue status. In our research, we found that the PERCLOS metric had a 0.79 to 0.87 correlation coefficient value which exceeds the 0.7 R value needed to be considered a strong correlation coefficient. A higher value than 0.7 indicates a more linear relationship which means that the metric is dependable [1].
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4 BibSonomy 
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Mr. Samuel Kim
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America
samuel.yj.kim@gmail.com, syjkim75@uw.edu
Mr. Irfan Wisanggeni
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America
Mr. Ryan Ros
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America
Ms Rania Hussein
Department of Electrical Engineering, University of Washington, Seattle, 98195, United States of America - United States of America


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