In order to equip the flight simulator with the corresponding gaze tracking system to further enhance the training quality for pilot cadets, a cross ratio-based gaze tracking system with an inexpensive monocular infrared camera and four infrared LEDs was proposed in this paper. Since it was difficult for traditional machine learning algorithms to extract eye features in the large field-of-view (FOV) infrared environment, a robust face detector based on deep neural network (DNN) was adopted to locate eyes with 68 facial landmarks more accurately. On this basis, the Gaussian Laplacian (LoG) blob detection algorithm was used to extract glints directly. Then the eye image was filtered by Gaussian to denoise and the pupil region was highlighted by global thresholding. Considering some areas of the pupil region were corroded by glints, the morphological operator closed operation was used to fill them. After that the pupil center was estimated by ellipse fitting. Finally, the gaze points were estimated by the constant cross ratio in the 2D projective space Through the experimental analysis after calibration, the system has high accuracy and practical value.


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    Title :

    Gaze tracking technology for flight simulator


    Contributors:
    Liu, Jiming (author) / Gu, Renshu (author)


    Publication date :

    2022-10-12


    Size :

    1883709 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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