Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions
Journal:
arXiv
Published Date:
Feb 14, 2025
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.
