EyeTheia：一个轻量级且易用的眼动追踪工具箱
EyeTheia: A Lightweight and Accessible Eye-Tracking Toolbox
AI总结：
提出基于网络摄像头的轻量级眼动追踪管道EyeTheia，结合MediaPipe特征提取和CNN模型，通过用户微调降低预测误差，在点探测任务中与商业方案表现一致。
摘要：
我们介绍了EyeTheia，一个用于基于网络摄像头的视线估计的轻量级开源深度学习管道，专为基于浏览器的实验平台和现实世界的认知与临床研究设计。EyeTheia仅使用标准笔记本电脑摄像头即可实现实时视线追踪，结合基于MediaPipe的 landmarks 提取和受iTracker启发的卷积神经网络，并支持可选的用户特定微调。我们研究了两种互补策略：在移动数据上预训练模型，以及在桌面数据集上从头训练相同架构。在MPIIFaceGaze上的验证结果显示，在标定前两种方法性能相当，而轻量级的用户特定微调持续降低了视线预测误差。我们还在一个真实的点探测任务中评估了EyeTheia，并与商业网络摄像头追踪器SeeSo SDK进行了比较。结果表明，在刺激呈现期间左右视线分配上具有高度一致性，尽管时间变异性更高。总体而言，EyeTheia为低成本视线追踪提供了一个透明且可扩展的解决方案，适用于可扩展和可重复的实验与临床研究。代码、训练模型和实验材料均已公开。
英文摘要：
We introduce EyeTheia, a lightweight and open deep learning pipeline for webcam-based gaze estimation, designed for browser-based experimental platforms and real-world cognitive and clinical research. EyeTheia enables real-time gaze tracking using only a standard laptop webcam, combining MediaPipe-based landmark extraction with a convolutional neural network inspired by iTracker and optional user-specific fine-tuning. We investigate two complementary strategies: adapting a model pretrained on mobile data and training the same architecture from scratch on a desktop-oriented dataset. Validation results on MPIIFaceGaze show comparable performance between both approaches prior to calibration, while lightweight user-specific fine-tuning consistently reduces gaze prediction error. We further evaluate EyeTheia in a realistic Dot-Probe task and compare it to the commercial webcam-based tracker SeeSo SDK. Results indicate strong agreement in left-right gaze allocation during stimulus presentation, despite higher temporal variability. Overall, EyeTheia provides a transparent and extensible solution for low-cost gaze tracking, suitable for scalable and reproducible experimental and clinical studies. The code, trained models, and experimental materials are publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
