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。