Design and Implementation of Deep Learning Based Pupil Tracking Technology for Application of Visible-Light Wearable Eye Tracker

In this paper, a deep-learning based pupil tracking technique is developed for the application of visible-light wearable eye trackers. By applying You only look once (YOLO) based deep learning object detection technology, the proposed pupil tracking method estimates and predicts the centers of pupil...

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Bibliographic Details
Main Authors: Kuo, Tzu-Ling, Fan, Chih-Peng
Format: Conference Proceeding
Language:English
Subjects:
Calibration
Conferences
Deep learning
Fitting
Gaze tracking
Object detection
Pupils
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Summary:In this paper, a deep-learning based pupil tracking technique is developed for the application of visible-light wearable eye trackers. By applying You only look once (YOLO) based deep learning object detection technology, the proposed pupil tracking method estimates and predicts the centers of pupils effectively in the visible-light mode. By testing the pupil tracking performance with the developed inference model, the precision is up to 80%, and the recall is close to 83%. Besides, the average horizontal and vertical pupil tracking errors of the deep-learning based design are only 4 pixels, which are much less than those of the previous ellipse fitting based design at the same visible-light conditions.
ISSN:2158-4001
DOI:10.1109/ICCE46568.2020.9043149