This paper describes a real-time online prototype automobile and truck driver-fatigue monitor. It uses remotely located charge-coupled-device cameras equipped with active infrared illuminators to acquire video images of the driver. Various visual cues that typically characterize the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver. The visual cues employed characterize eyelid movement, gaze movement, head movement, and facial expression. A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained. The simultaneous use of multiple visual cues and their systematic combination yields a much more robust and accurate fatigue characterization than using a single visual cue. This system was validated under real-life fatigue conditions with human subjects of different ethnic backgrounds, genders, and ages; with/without glasses; and under different illumination conditions. It was found to be reasonably robust, reliable, and accurate in fatigue characterization.


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    Title :

    Real-Time Eye Detection and Tracking under Various Light Conditions


    Contributors:
    Jiao, Feng (author) / He, Guiming (author)

    Publication date :

    2007-10-05


    Remarks:

    doi:10.2481/dsj.6.S636
    Data Science Journal; Vol 6 (2007); S636-S640 ; 1683-1470



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 669



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