Driver alert state and fatigue detection by salient points analysis

Jimenez-Pinto, Javier; Torres-Torriti, Miguel

doi:10.1109/icsmc.2009.5346760

Cited by 20 publications

(11 citation statements)

References 16 publications

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“…Yawning is classified based on the change of the texture in the corner of the mouth while the mouth is opened widely. Moreover, as in the case implemented for measuring eye activities, Jiménez-Pinto et al [13] have detected the yawn based upon the salient points in the mouth region. These salient points introduced by Shi and Tomasi [14] are able to describe the motion of the lips movement, and yawn is determined by examining the motion in the mouth region.…”

Section: Related Workmentioning

confidence: 99%

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Yawn analysis with mouth occlusion detection

Ibrahim

Soraghan

Petropoulakis

et al. 2015

Biomedical Signal Processing and Control

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One of the most common signs of tiredness or fatigue is yawning. Naturally, identification of fatigued individuals would be helped if yawning is detected. Existing techniques for yawn detection are centred on measuring the mouth opening. This approach, however, may fail if the mouth is occluded by the hand, as it is frequently the case. The work presented in this paper focuses on a technique to detect yawning whilst also allowing for cases of occlusion. For measuring the mouth opening, a new technique which applies adaptive colour region is introduced. For detecting yawning whilst the mouth is occluded, Local Binary Pattern (LBP) features are used to also identify facial distortions during yawning. In this research, the Strathclyde Facial Fatigue (SFF) database which contains genuine video footage of fatigued individuals is used for training, testing and evaluation of the system

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Section: Related Workmentioning

confidence: 99%

“…LBP ri P,R = min ROR LBP P,R , i i = 0, 1, ..., P − 1 (13) where ROR(x, i) performs a circular bitwise right shift on the P-bit number x with i time. A LBP uniform pattern (u2), LBP u2 (P,R) was also proposed.…”

Section: Covered Mouth Detectionmentioning

confidence: 99%

Yawn analysis with mouth occlusion detection

Ibrahim

Soraghan

Petropoulakis

et al. 2015

Biomedical Signal Processing and Control

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“…All the first generations of lane detection systems wereedge-based. They relied on thresholding the image intensity to detectpotential lane edges, followed by a perceptual grouping of the edge points to detect the lane markers of interest [3]. The work in Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm proposed that region of interest (ROI) can be extracted for lane departure.…”

Section: Literature Surveymentioning

confidence: 99%

Real Time Driver Fatigue Recognition Based on Image Processing

Ingale¹,

Wasnik²,

Peddawad³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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Driver fatigue is the main reason of serious damages among all other road accidents. Thus, a new system is proposed with a modern approach which will detect driver fatigue by considering most of the fatigue symptoms such as eye closure, yawning, head tilting. Inattentive vehicle movement on the road under fatigue condition is also considerable for driver fatigue. The approach of this system is to detect these symptoms for the best driving condition on the road. These symptoms are monitored by using two cameras. Thus, a robust system is proposed where head tilting, facial expressions and lane departure for fatigue will be detected collectively. First step of this system is to visualize the face of the driver from one camera while another camera is used to track the road. Then face, eye and mouth (check for yawning) detections of the driver are performed. Facial detection of our method is free from any kind of wrong or bogus detection. Besides, the detection process can strongly determine the desired part of the face in spite of poor light intensity of camera and more realistic to implement. Head tilting detection makes this approach more promising. Lane departure detection in the approach gives this system an advanced level. Lane detection measures the distance of the vehicle between two lanes. Alarm system has been made which will be activated if any fault is found through these detection procedures.

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“…In some recent researches such as [73,74,75], the salient points of face are detected after face detection. In these researches, the salient points are tracked over time.…”

Section: Salient Points Detectionmentioning

confidence: 99%