论文标题
时间信息的好处用于基于外观的凝视估计
Benefits of temporal information for appearance-based gaze estimation
论文作者
论文摘要
通常基于深度学习技术的最先进的外观凝视估计方法主要依赖于静态特征。但是，眼睛凝视的时间痕迹包含有用的信息来估计给定的凝视点。例如，当应用于远程或低分辨率的图像方案时，使用顺序的眼睛凝视信息的方法显示出令人鼓舞的结果。对于更高的分辨率/框架速率成像系统，时间凝视痕迹的贡献幅度尚不清楚，其中捕获了有关眼睛的更多详细信息。在本文中，我们调查了使用高分辨率，高框架头部安装的虚拟现实系统捕获的眼图的时间序列，可以利用以增强基于端到端的基于外观的深度学习模型的准确性。将性能与模型的静态版本进行比较。结果证明了时间信息的统计学上很明显的好处，特别是对于凝视的垂直成分。
State-of-the-art appearance-based gaze estimation methods, usually based on deep learning techniques, mainly rely on static features. However, temporal trace of eye gaze contains useful information for estimating a given gaze point. For example, approaches leveraging sequential eye gaze information when applied to remote or low-resolution image scenarios with off-the-shelf cameras are showing promising results. The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured. In this paper, we investigate whether temporal sequences of eye images, captured using a high-resolution, high-frame rate head-mounted virtual reality system, can be leveraged to enhance the accuracy of an end-to-end appearance-based deep-learning model for gaze estimation. Performance is compared against a static-only version of the model. Results demonstrate statistically-significant benefits of temporal information, particularly for the vertical component of gaze.
