Applications of driver's line of sight to automobiles-what can driver's eye tell

Saito, Haruki; Ishiwaka, T.; Okabayashi, Shigeru

doi:10.1109/vnis.1994.396872

Cited by 18 publications

(11 citation statements)

References 5 publications

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“…This approach is non-intrusive and becomes more and more practical with the rapid development of camera and computer vision technology. Several studies have demonstrated their feasibility and some of them claimed that their systems perform as effectively as the systems detecting physiological signals do (Saito et al, 1994;Ueno et al, 1994;Boverie et al, 1998;Grace, 2001). However, efforts in this direction are often directed to detecting a single visual cue such as eyelid movement.…”

Section: User Stress and Fatigue Recognitionmentioning

confidence: 99%

Toward a decision-theoretic framework for affect recognition and user assistance

Liao

Zhang

Zhu

et al. 2006

International Journal of Human-Computer Studies

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There is an increasing interest in developing intelligent human-computer interaction systems that can fulfill two functions -recognizing user affective states and providing the user with timely and appropriate assistance. In this paper, we present a general unified decision-theoretic framework based on Influence Diagrams for simultaneously modeling user affect recognition and assistance. Affective state recognition is achieved through active probabilistic inference from the available multi-modality sensory data. User assistance is automatically accomplished through a decisionmaking process that balances the benefits of keeping the user in productive affective states and the costs of performing user assistance. We discuss three theoretical issues within the framework, namely, user affect recognition, active sensory action selection, and user assistance. Validation of the proposed framework via a simulation study demonstrates its capability in efficient user affect recognition as well as timely and appropriate user assistance. Besides the theoretical contributions, we build a non-invasive real-time prototype system to recognize different user affective states (stress and fatigue) from four-modality user measurements, namely physical appearance features, physiological measures, user performance, and behavioral data. The affect recognition component of the prototype system is subsequently validated through a real world study involving human subjects.

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Section: User Stress and Fatigue Recognitionmentioning

confidence: 99%

Toward a decision-theoretic framework for affect recognition and user assistance

Liao

Zhang

Zhu

et al. 2006

International Journal of Human-Computer Studies

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“…Many efforts [1][2][3][4][5][6][7][8][9] have been reported in the literature for developing active safety systems intended for reducing the number of automobile accidents due to reduced vigilance. Among different techniques, the best detection accuracy is achieved with techniques that measure physiological conditions like brain waves, heart rate, and pulse rate [8,10].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Ishii et al [7] introduced a system for characterizing a driver's mental state from his facial expression. Saito et al [1] proposed a vision system to detect a driver's physical and mental conditions from line of sight (gaze). Boverie et al [3] described a system for monitoring driving vigilance by studying the eyelid movement.…”

Section: Introductionmentioning

confidence: 99%

“…With the ever-growing traffic conditions, this problem will further deteriorate. For this reason, developing systems actively monitoring a driver's level of vigilance and alerting the driver of any insecure driving conditions is essential to prevent accidents.Many efforts [1][2][3][4][5][6][7][8][9] have been reported in the literature for developing active safety systems intended for reducing the number of automobile accidents due to Real-Time Imaging 8, 357-377 (2002Imaging 8, 357-377 ( ) doi:10.1006Imaging 8, 357-377 ( /rtim.2002, available online at http://www.idealibrary.com on reduced vigilance. Among different techniques, the best detection accuracy is achieved with techniques that measure physiological conditions like brain waves, heart rate, and pulse rate [8,10] People in fatigue exhibit certain visual behaviors easily observable from changes in their facial features like the eyes, head, and face.…”

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confidence: 99%

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Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance

2002

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T his paper describes a real-time prototype computer vision system for monitoring driver vigilance. The main components of the system consists of a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver's level of vigilance. The visual behaviors include eyelid movement, face orientation, and gaze movement (pupil movement). The system was tested in a simulating environment with subjects of different ethnic backgrounds, different genders, ages, with/without glasses, and under different illumination conditions, and it was found very robust, reliable and accurate. IntroductionThe ever-increasing number of traffic accidents in the US due to a diminished driver's vigilance level has become a problem of serious concern to society. Drivers with a diminished vigilance level suffer from a marked decline in their abilities of perception, recognition, and vehicle control, and therefore pose serious danger to their own life and the lives of other people. Statistics show that a leading cause for fatal or injury-causing traffic accidents is due to drivers with a diminished vigilance level. In the trucking industry, 57% fatal truck accidents are due to driver fatigue. It is the number 1 cause for heavy truck crashes. Seventy percent of American drivers report driving fatigued. With the ever-growing traffic conditions, this problem will further deteriorate. For this reason, developing systems actively monitoring a driver's level of vigilance and alerting the driver of any insecure driving conditions is essential to prevent accidents.Many efforts [1][2][3][4][5][6][7][8][9] have been reported in the literature for developing active safety systems intended for reducing the number of automobile accidents due to Real-Time Imaging 8, 357-377 (2002Imaging 8, 357-377 ( ) doi:10.1006Imaging 8, 357-377 ( /rtim.2002, available online at http://www.idealibrary.com on reduced vigilance. Among different techniques, the best detection accuracy is achieved with techniques that measure physiological conditions like brain waves, heart rate, and pulse rate [8,10] People in fatigue exhibit certain visual behaviors easily observable from changes in their facial features like the eyes, head, and face. Typical visual characteristics observable from the image of a person with reduced alertness level include slow eyelid movement [12,13], smaller degree of eye openness (or even closed), frequent nodding [14], yawning, gaze (narrowness in the line of sight), sluggish in facial expression, and sagging posture. To make use of these visual cues, another increasingly popular and non-invasive approach for monitoring fatigue is to assess a driver's vigilance level through visual observation of his/her physical conditions using a camera and state-of-the-art technologies in computer vision. ...

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“…Ishii et al [13] introduced a system for characterizing a driver's mental state from his facial expression. A fatigue monitoring system based on line of sight of gaze was reported by Satio et al [10] Eyelid movements have also been studied by Bovere et al [12] and has been successfully used in fatigue monitoring. The results obtained so far have been quite promising in detecting driver fatigue.…”

Section: Introductionmentioning

confidence: 99%

A Non Intrusive Human Fatigue Monitoring System

Ali¹,

Sarkar²,

Kumar³

et al. 2012

IJFCC

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Abstract-An algorithm for the real time detection of human fatigue has been developed by using multiple fatigue parameters. In addition to existing metrics such as Blink rate and the PERCLOS, a third metric, the variation of head position and the tilt has been used in generating a cumulative measure of fatigue with non intrusive techniques of monitoring. The initial results obtained out of the simulation studies have been discussed in this paper with a stress on fatigue monitoring using variation in head position. The system was found to be sufficiently accurate and highly robust in prediction of fatigue under test situations for fatigue monitoring.

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Applications of driver's line of sight to automobiles-what can driver's eye tell

Cited by 18 publications

References 5 publications

Toward a decision-theoretic framework for affect recognition and user assistance

Toward a decision-theoretic framework for affect recognition and user assistance

Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance

A Non Intrusive Human Fatigue Monitoring System

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