Abstract
In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.
Abstract (translated)
近年来,我们在手持移动设备上见证了越来越多的交互系统,这些系统利用视口作为单一或补充交互方式。这一趋势是由这些设备的增强计算能力、其摄像头的高分辨率和容量以及从高级机器学习技术中获得的改善视口估计精度所获得的提高所驱动的。随着文献的快速进展,迫切需要审查最新进展、确定边界并识别视口估计和交互方面的 key research挑战和机会。本 paper 旨在实现这一目标,通过呈现这一领域的 end-to-end 整体视角,从视口捕捉传感器到视口估计工作流程、再到深度学习技术和视口交互应用程序。
URL
https://arxiv.org/abs/2307.00122
