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International Journal of Vehicular Technology
Article . 2014 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Driver’s Fatigue Detection Based on Yawning Extraction

Authors: Nawal Alioua; Aouatif Amine; Mohammed Rziza;

Driver’s Fatigue Detection Based on Yawning Extraction

Abstract

The increasing number of traffic accidents is principally caused by fatigue. In fact, the fatigue presents a real danger on road since it reduces driver capacity to react and analyze information. In this paper we propose an efficient and nonintrusive system for monitoring driver fatigue using yawning extraction. The proposed scheme uses face extraction based support vector machine (SVM) and a new approach for mouth detection, based on circular Hough transform (CHT), applied on mouth extracted regions. Our system does not require any training data at any step or special cameras. Some experimental results showing system performance are reported. These experiments are applied over real video sequences acquired by low cost web camera and recorded in various lighting conditions.

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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!
75
Top 1%
Top 1%
Top 10%
gold