Abstract
Mobile gaze tracking involves inferring a user's gaze point or direction on a mobile device's screen from facial images captured by the device's front camera. While this technology inspires an increasing number of gaze-interaction applications, achieving consistent accuracy remains challenging due to dynamic user-device spatial relationships and varied motion conditions inherent in mobile contexts. This paper provides empirical evidence on how user mobility and behaviour affect mobile gaze tracking accuracy. We conduct two user studies collecting behaviour and gaze data under various motion conditions - from lying to maze navigation - and during different interaction tasks. Quantitative analysis has revealed behavioural regularities among daily tasks and identified head distance, head pose, and device orientation as key factors affecting accuracy, with errors increasing by up to 48.91% in dynamic conditions compared to static ones. These findings highlight the need for more robust, adaptive eye-tracking systems that account for head movements and device deflection to maintain accuracy across diverse mobile contexts.
Abstract (translated)
移动凝视追踪技术通过分析设备前置摄像头捕捉到的面部图像来推断用户在移动设备屏幕上注视点或方向。虽然这项技术激发了越来越多以凝视为交互的应用程序，但由于移动环境中的用户与设备之间的空间关系不断变化以及运动条件多样，保持高精度仍然是一个挑战。本文提供了实证证据，说明用户的移动行为如何影响移动凝视追踪的准确性。我们进行了两项用户研究，在各种不同的运动条件下（从躺卧到迷宫导航）和不同交互任务期间收集了行为和注视数据。定量分析揭示了日常任务中的行为规律，并确定头部距离、头部姿态以及设备方向是影响精度的关键因素，与静态条件相比，在动态条件下错误率最多可增加48.91%。这些发现强调需要开发更加稳健且适应性强的眼动追踪系统，以应对头部运动和设备偏移的影响，从而在多种移动环境中保持准确性。
URL
https://arxiv.org/abs/2502.10570
