Yawning Detection for Monitoring Driver Fatigue

Fan, Xiao; Yin, Baocai; Sun, Yanfeng

doi:10.1109/icmlc.2007.4370228

Cited by 91 publications

(60 citation statements)

References 7 publications

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“…In the method studied by Fan et al [27], the driver's face was detected by using a Gravity-Center template. Jimenez et al [28] described a method of fatigue detection based on the percentage of closing eyes and detection of yawning and nodding. The goal of face detection is to categorize all image regions that restrain a face in spite of of its position, orientation, and lighting conditions.…”

Section: Figure [1] System Architecturementioning

confidence: 99%

“…A linear arrangement of the facial appearance to detect the yawning mouth was main focus of [16] who took benefit of grey protuberance and Gabor wavelets to distinguish the mouth corners and used LDA. [17] proposed a system to detect the face using ViolaJones technique and extracted the mouth region, in which lips were searched in the course of spatial fuzzy c-means (s-FCM) clustering. [18] exploited mouth geometrical features to detect yawning.…”

Section: Figure [1] System Architecturementioning

confidence: 99%

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Hypovigilant System: An Approach for Lethargy Detection

Bhor¹,

Mahajan²,

Dharmadhikari³

et al. 2016

IJCA

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There is much attentional impairment while driving that affect driver's reaction. Among which driving while lethargic is one of the major causes behind road accidents, and exposes the driver to a large extent of crash risk compared to driving while alert. Therefore, the use of an assistive system that monitor a driver's level of vigilance and alert the driver in case of lethargy can be significant in the prevention of accidents. This paper introduces a new approach towards detection of driver's lethargy based on yawning measurement and head movement. This involves several steps which includes the real time detection and tracking of head movement, the detection of yawning based on measuring the amount of changes in the mouth contour area. Test results express that the proposed system is able to measure the aforesaid parameters and detect driver's lethargy.

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Section: Figure [1] System Architecturementioning

confidence: 99%

Section: Figure [1] System Architecturementioning

confidence: 99%

Hypovigilant System: An Approach for Lethargy Detection

Bhor¹,

Mahajan²,

Dharmadhikari³

et al. 2016

IJCA

View full text Add to dashboard Cite

show abstract

“…Then, postprocessing techniques are applied to reduce problems that arise in presence of glasses, reflections inside glasses, or prominent eyebrows. Fan et al [21] locate and track a driver mouth movement to monitor yawning. They detect face using gravity-center template.…”

Section: Related Workmentioning

confidence: 99%

Driver’s Fatigue Detection Based on Yawning Extraction

Alioua

Amine

Rziza

2014

International Journal of Vehicular Technology

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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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“…In their method, one low resolution camera was installed in the car to supply the driver's head position and one high resolution camera to locate the mouth region in each frame. In the method studied by Fan et al [27], the driver's face was detected by using a Gravity-Center template. Jimenez et al [28] described a new method of fatigue detection in drivers based on the percentage of closing eyes and detection of yawning and nodding.…”

Section: Figure 2 Face Detectionmentioning

confidence: 99%

“…The Bayesian decision theory was used to classify the face or nonface pattern and also to improve the correct rate of face detection. [16] took advantage of grey projection and Gabor wavelets to detect the mouth corners and uses LDA to find a linear combination of the those features to detect the yawning mouth. [17] detects the face using Viola-Jones technique and extracts the mouth region, in which lips were searched for through spatial fuzzy c-means (s-FCM) clustering.…”

Section: Figure 2 Face Detectionmentioning

confidence: 99%

Survey on Driver’s Drowsiness Detection System

Dharmadhikari¹,

Bhor²,

Mahajan³

et al. 2015

IJCA

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There is much attentional impairment while driving that affect driver's reaction. Among which driving while drowsy is one of the major causes behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of an assistive system that monitor a driver's level of vigilance and alert the driver in case of drowsiness can be significant in the prevention of accidents. This paper introduces a new approach towards detection of driver's drowsiness based on yawning measurement and head movement. This involves several steps including the real time detection and tracking of driver's face, detection and tracking of the mouth contour, the detection of yawning based on measuring both the rate and the amount of changes in the mouth contour area and head movement tracking. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect driver's drowsiness.

show abstract

Yawning Detection for Monitoring Driver Fatigue

Cited by 91 publications

References 7 publications

Hypovigilant System: An Approach for Lethargy Detection

Hypovigilant System: An Approach for Lethargy Detection

Driver’s Fatigue Detection Based on Yawning Extraction

Survey on Driver’s Drowsiness Detection System

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