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Driver Fatigue Detection Based on Eye Status

Authors: Sanyuan Zhao; Tin Shen;

Driver Fatigue Detection Based on Eye Status

Abstract

It designed a driver fatigue detection system using the eye's open and close status for decision. It firstly uses an adaptive skin color region for face segmentation. After that, the face region gradient is calculated and adaptively thresholded. It uses a color HOG algorithm to pick out the eye region features. All eye feature vectors are pushed into the linear SVM classifier for training. Two stages classifiers are designed. The first SVM classifier is used to detect the location of eyes, and the second one is used to decide whether the eye is closed or open. In a period of time, if the driver is detected a high eye close rate or a long time without the eye closed, the system will make an alarm.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
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