Driver blink measurement by the motion picture processing and its application to drowsiness detection

Itö, Tatsuo; Mita, S.; Kozuka, Kazuhiro; Nakano, Tomoaki; Yamamoto, Shin

doi:10.1109/itsc.2002.1041208

Cited by 43 publications

(16 citation statements)

References 2 publications

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“…Using video recordings, eyelid movement is visible in the images and can be assessed using image processing methods. Different algorithms for that purpose are based on either the motion detection derived from differencing two consecutive images (e.g., Bhaskar, Keat, Ranganath, & Venkatesh, 2003;Chau & Betke, 2005;Fogelton & Benesova, 2016;Jiang, Tien, Huang, Zheng, & Atkins, 2013), a second-order derivative method of image differentiations (Gorodnichy, 2003), a state classification (e.g., Choi, Han, & Kim, 2011;Missimer & Betke, 2010;Pan, Sun, & Wu, 2008;Pan, Sun, Wu, & Lao, 2007), an evaluation of the color contrast or amount of visible color of specific eye regions (Cohn, Xiao, Moriyama, Ambadar, & Kanade, 2003;Danisman, Bilasco, Djeraba, & Ihaddadene, 2010;Lee, Lee, & Park, 2010), the distance between landmarks or arcs representing the upper and lower eyelid (Fuhl et al, 2016;Ito, Mita, Kozuka, Nakano, & Yamamoto, 2002;Miyakawa, Takano, & Nakamura, 2004;Moriyama et al, 2002;Sukno, Pavani, Butakoff, & Frangi, 2009), the missing regions of the open eye like the iris or pupil due to their occlusion by the upper and lower eyelid (Hansen & Pece, 2005;Pedrotti, Lei, Dzaack, & Rötting, 2011), or a combination of the described methods (Sirohey, Rosenfeld, & Duric, 2002). Instead of measuring the real distance between the upper and lower eyelid, most of these algorithms use an indirect measure (motion detection, classification, color contrast, missing eye regions) to conclude whether the eye is closed.…”

Section: Blink Detection Methodsmentioning

confidence: 99%

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

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In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.

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Section: Blink Detection Methodsmentioning

confidence: 99%

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

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“…10,13,19,29,35,37 The comparison of the approaches is not easy, because results are reported in different nonstandard ways. 5,8,9,15,34 For a comprehensive survey on eye and gaze tracking models and approaches see ref. 8,27 However, it is possible to say that approaches based on color analysis are limited by illumination conditions and thus cannot be employed at night.…”

Section: State Of the Artmentioning

confidence: 99%

Face salient points and eyes tracking for robust drowsiness detection

Jimenez-Pinto¹,

Torres-Torriti²

2011

Robotica

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Link to this article: http://journals.cambridge.org/abstract_S0263574711000749How to cite this article: J. Jimenez-Pinto and M. Torres-Torriti (2012). Face salient points and eyes tracking for robust drowsiness detection. SUMMARYMeasuring a driver's level of attention and drowsiness is fundamental to reducing the number of traffic accidents that often involve bus and truck drivers, who must work for long periods of time under monotonous road conditions. Determining a driver's state of alert in a noninvasive way can be achieved using computer vision techniques. However, two main difficulties must be solved in order to measure drowsiness in a robust way: first, detecting the driver's face location despite variations in pose or illumination; secondly, recognizing the driver's facial cues, such as blinks, yawns, and eyebrow rising. To overcome these challenges, our approach combines the well-known Viola-Jones face detector with the motion analysis of Shi-Tomasi salient features within the face. The location of the eyes and blinking is important to refine the tracking of the driver's head and compute the so-called PERCLOS, which is the percentage of time the eyes are closed over a given time interval. The latter cue is essential for noninvasive driver's alert state estimation as it has a high correlation with drowsiness. To further improve the location of the eyes under different conditions of illumination, the proposed method takes advantage of the high reflectivity of the retina to near infrared illumination employing a camera with an 850 nm wavelength filter. The paper shows that motion analysis of the salient points, in particular cluster mass centers and spatial distributions, yields better head tracking results compared to the state-ofthe-art and provides measures of the driver's alert state.

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“…The pupil's estimated position can in this case be found aŝ e s = c s k +d s k (11) whered s k is an estimate of the pupil's location vector relative to the centroid. An estimation of the relative position vector d s k is found by means of a simple fi rst-order IIR running average fi lter of the form…”

Section: A Eyes Location and Head-pose Estimationmentioning

confidence: 99%

“…This has motivated some researchers to use near infrared (IR) cameras, exploiting the retinas' high reflectivity to 850 nm wavelength illumination [8], [14]. Some approaches employ neural-networks to extract the head and main features [4], [23], while other rely on a variety of template matching schemes [2], [6], [5], [11], [26].…”

Section: Introductionmentioning

confidence: 99%

Driver alert state and fatigue detection by salient points analysis

Jimenez-Pinto

Torres-Torriti

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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Assessing a driver's state of awarness and fatigue is especially important to reduce the number of traffic accidents often involving bus and truck drivers, who must work during several hours under monotonous road conditions. Two main challenges arise in resolving the state of alert: first, the system must be capable of detecting the driver's face location; secondly, the driver's facial cues, such as blinking, yawning, and eyebrow rising must be recognized. Our approach combines the wellknown Viola-Jones face detector with motion analysis of ShiTomasi salient features within the face to determine the driver's state of alert. The location of the eyes and blinking are cues whose detection is also important. To this end, the proposed method takes advantage of the high reflectivity of the retina to near infrared illumination employing a camera with an 850 nm wavelength filter. Motion analysis of the salient points, in particular cluster mass centers and spatial distribution, has proved successful in determining the driver's state of alert.

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Driver blink measurement by the motion picture processing and its application to drowsiness detection

Cited by 43 publications

References 2 publications

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

Face salient points and eyes tracking for robust drowsiness detection

Driver alert state and fatigue detection by salient points analysis

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