In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

中文翻译：

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基于深度学习的可见光可穿戴式注视跟踪设备的学生中心检测和跟踪技术

在这项研究中，为应用可见光可穿戴式眼动仪，开发了一种基于深度学习技术的瞳孔跟踪方法。通过应用基于“一次只看一次”（YOLO）模型的深度学习对象检测技术，所提出的瞳孔跟踪方法可以在可见光模式下有效地估计和预测瞳孔的中心。通过使用开发的基于YOLOv3-tiny的模型测试瞳孔跟踪性能，检测精度高达80％，召回率接近83％。此外，建议的基于YOLO的深度学习设计的平均可见光瞳孔跟踪误差在训练模式下小于2个像素，在跨人测试中小于5个像素，与以前的椭圆拟合设计相比，它们要小得多，而在相同的可见光条件下，无需使用深度学习技术。结合校准过程后，基于YOLOv3-tiny的瞳孔跟踪模型在训练和测试模式下的平均凝视跟踪误差分别小于2.9度和3.5度，并且该可见光可穿戴式凝视跟踪系统在在基于GPU的软件嵌入式平台上，每秒高达20帧（FPS）。