用事件相机数据实现低功耗眼动追踪，可在微型控制器上运行。
EETnet: a CNN for Gaze Detection and Tracking for Smart-Eyewear
- 基于事件数据设计轻量卷积网络，适配资源受限设备。
- 提出分类与回归双版本模型，支持网格定位与像素级追踪。
- 提供完整训练、评估与量化流程，便于嵌入式部署。
事件相机正成为高效低功耗眼动追踪的流行方案。由于事件数据具有稀疏性和异步性，处理所需算力更低，延迟可达微秒级。然而，许多现有方法仅在高性能GPU上验证，未实现在真实嵌入式设备上的部署。本文提出EETnet，一种仅使用事件数据的眼动追踪卷积神经网络，可运行于资源受限的微控制器。我们还提出一套完整的训练、评估与量化方法，基于公开数据集实现。最后，设计了两种架构：一种是将瞳孔定位在图像叠加网格上的分类模型，另一种是在像素级别进行回归的模型。
原文摘要 · Abstract (English)
Event-based cameras are becoming a popular solution for efficient, low-power eye tracking. Due to the sparse and asynchronous nature of event data, they require less processing power and offer latencies in the microsecond range. However, many existing solutions are limited to validation on powerful GPUs, with no deployment on real embedded devices. In this paper, we present EETnet, a convolutional neural network designed for eye tracking using purely event-based data, capable of running on microcontrollers with limited resources. Additionally, we outline a methodology to train, evaluate, and quantize the network using a public dataset. Finally, we propose two versions of the architecture: a classification model that detects the pupil on a grid superimposed on the original image, and a regression model that operates at the pixel level.
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