Algorithms for the estimation of gaze direction from mobile and video-based
eye trackers typically involve tracking a feature of the eye that moves through
the eye camera image in a way that covaries with the shifting gaze direction,
such as the center or boundaries of the pupil. Tracking these features using
traditional computer vision techniques can be difficult due to partial
occlusion and environmental reflections. Although recent efforts to use machine
learning (ML) for pupil tracking have demonstrated superior results when
evaluated using standard measures of segmentation performance, little is known
of how these networks may affect the quality of the final gaze estimate. This
work provides an objective assessment of the impact of several contemporary
ML-based methods for eye feature tracking when the subsequent gaze estimate is
produced using either feature-based or model-based methods. Metrics include the
accuracy and precision of the gaze estimate, as well as drop-out rate.

通过比较不同机器学习方法在眼部特征追踪中的表现，本研究客观评估了这些方法对最终注视估计质量的影响。

使用深度学习提高虚拟现实中眼动追踪的鲁棒性、准确性和精度

Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and  Precision in Virtual Reality

This paper presents a sparse Change-Based Convolutional Long Short-Term
Memory (CB-ConvLSTM) model for event-based eye tracking, key for
next-generation wearable healthcare technology such as AR/VR headsets. We
leverage the benefits of retina-inspired event cameras, namely their
low-latency response and sparse output event stream, over traditional
frame-based cameras. Our CB-ConvLSTM architecture efficiently extracts
spatio-temporal features for pupil tracking from the event stream,
outperforming conventional CNN structures. Utilizing a delta-encoded recurrent
path enhancing activation sparsity, CB-ConvLSTM reduces arithmetic operations
by approximately 4.7$\times$ without losing accuracy when tested on a
\texttt{v2e}-generated event dataset of labeled pupils. This increase in
efficiency makes it ideal for real-time eye tracking in resource-constrained
devices. The project code and dataset are openly available at
https://github.com/qinche106/cb-convlstm-eyetracking.

本研究提出了一种用于事件驱动眼动追踪的稀疏变化卷积长短期记忆（CB-ConvLSTM）模型，对于下一代可穿戴医疗技术如 AR/VR 头戴式设备至关重要。该模型利用了受视网膜启发的事件相机的优势，即低延迟响应和稀疏输出事件流，超过传统基于帧的摄像机。CB-ConvLSTM 架构能从事件流中高效提取时空特征进行瞳孔跟踪，胜过传统 CNN 结构。通过利用增强稀疏激活的增量编码循环路径，CB-ConvLSTM 在经过标记的瞳孔数据集上测试时能减少约 4.7 倍的算术运算而不损失精度。这种提高效率的进展使得它非常适合在资源受限设备上进行实时眼动追踪。项目代码和数据集可以在 https://github.com/qinche106/cb-convlstm-eyetracking 获取。