GaVe：一种使用单点校准的基于网络摄像头的视线售货界面
GaVe: A Webcam-Based Gaze Vending Interface Using One-Point Calibration
AI总结：
本文提出基于普通网络摄像头和单点校准的非接触式视线售货界面GaVe，通过分层商品簇与注视停留选择实现操作，并经22人用户研究验证其可用性与性能。
摘要：
甚至在Covid-19疫情之前，人们就已经探索了卫生、非接触式人机交互的有益用例。视线输入，即通过用户的眼部运动进行信息输入，代表了人机系统中非接触式交互的一种有前景的方法。在本文中，我们提出了GazeVending界面（GaVe），它让用户能够用眼睛控制显示器上的动作。该界面可在普通网络摄像头上运行，而网络摄像头在当今大多数笔记本电脑上都有配备，并且在使用前仅需进行一次单点校准。GaVe采用分层结构设计，首先向用户呈现宽泛的商品簇，随后引导他们完成另一轮选择，从而能够呈现大量商品。GaVe中的簇/商品选择基于注视的停留时间，即用户注视给定簇/商品的持续时间。研究开展了一项用户研究（N=22），以测试最佳停留时间阈值和舒适的人到显示器距离。研究记录了用户对系统的感知，以及错误率和任务完成时间。我们发现，所有参与者经过短时间训练后都能够使用该系统，并在系统使用过程中表现出良好性能，平均在6.76秒内从一组12件商品中选出目标商品。参与者能够快速理解并知道如何与该界面交互。我们提供了GaVe的设计指南，并讨论了该系统的潜力。
英文摘要：
Even before the Covid-19 pandemic, beneficial use cases for hygienic, touchless human-machine interaction have been explored. Gaze input, i.e., information input via eye-movements of users, represents a promising method for contact-free interaction in human-machine systems. In this paper, we present the GazeVending interface (GaVe), which lets users control actions on a display with their eyes. The interface works on a regular webcam, available on most of today's laptops, and only requires a one-point calibration before use. GaVe is designed in a hierarchical structure, presenting broad item cluster to users first and subsequently guiding them through another selection round, which allows the presentation of a large number of items. Cluster/item selection in GaVe is based on the dwell time of fixations, i.e., the time duration that users look at a given Cluster/item. A user study (N=22) was conducted to test optimal dwell time thresholds and comfortable human-to-display distances. Users' perception of the system, as well as error rates and task completion time were registered. We found that all participants were able to use the system with a short time training, and showed good performance during system usage, selecting a target item within a group of 12 items in 6.76 seconds on average. Participants were able to quickly understand and know how to interact with the interface. We provide design guidelines for GaVe and discuss the potentials of the system.
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。
