How far are we from quantifying visual attention in mobile HCI?
원문 초록
With an ever-increasing number of mobile devices competing for our attention, quantifying when, how often, or for how long users visually attend to their devices has emerged as a core challenge in mobile human-computer interaction. Encouraged by recent advances in automatic eye contact detection using machine learning and device-integrated cameras, we provide a fundamental investigation into the feasibility of quantifying visual attention during everyday mobile interactions. We identify core challenges and sources of errors associated with sensing attention on mobile devices in the wild, including the impact of face and eye visibility, the importance of robust head pose estimation, and the need for accurate gaze estimation. Based on this analysis, we propose future research directions and discuss how eye contact detection represents the foundation for exciting new applications towards next-generation pervasive attentive user interfaces.
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DMAGaze: Gaze Estimation Based on Feature Disentanglement and Multi-Scale Attention
From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation
Event-based Egocentric Human Pose Estimation in Dynamic Environment
Head Pose Estimation Based on 5D Rotation Representation
Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency
DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
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