用三台摄像头实现无需复杂校准的高精度眼球追踪。
Tri-Cam: Practical Eye Gaze Tracking via Camera Network
- 三台廉价摄像头构建网络，分治眼动追踪任务。
- 相比商用设备精度相当，支持更大自由活动范围。
- 通过鼠标点击自动校准，用户操作更简便。
人眼是传递情绪、意图乃至健康状况的重要窗口，眼动追踪在人机交互与心理医学研究中具有广泛应用。然而现有方案难以应对用户自由移动，且需繁琐校准。本文提出Tri-Cam，基于三台低成本RGB摄像头的深度学习眼动追踪系统。其采用分层网络结构提升训练效率，并针对分离任务设计专用模块。系统集成隐式校准模块，利用鼠标点击时机降低用户端校准负担。在与顶尖商用设备Tobii的对比测试中，Tri-Cam达到相当精度，同时支持更大自由活动区域。结果表明，Tri-Cam提供了一种用户友好、经济可靠的眼动追踪方案，具备广泛实际应用潜力。
原文摘要 · Abstract (English)
As human eyes serve as conduits of rich information, unveiling emotions, intentions, and even aspects of an individual's health and overall well-being, gaze tracking also enables various human-computer interaction applications, as well as insights in psychological and medical research. However, existing gaze tracking solutions fall short at handling free user movement, and also require laborious user effort in system calibration. We introduce Tri-Cam, a practical deep learning-based gaze tracking system using three affordable RGB webcams. It features a split network structure for efficient training, as well as designated network designs to handle the separated gaze tracking tasks. Tri-Cam is also equipped with an implicit calibration module, which makes use of mouse click opportunities to reduce calibration overhead on the user's end. We evaluate Tri-Cam against Tobii, the state-of-the-art commercial eye tracker, achieving comparable accuracy, while supporting a wider free movement area. In conclusion, Tri-Cam provides a user-friendly, affordable, and robust gaze tracking solution that could practically enable various applications.
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