Most monocular depth sensing methods use conventionally captured images that
are created without considering scene content. In contrast, animal eyes have
fast mechanical motions, called saccades, that control how the scene is imaged
by the fovea, where resolution is highest. In this paper, we present the
SaccadeCam framework for adaptively distributing resolution onto regions of
interest in the scene. Our algorithm for adaptive resolution is a
self-supervised network and we demonstrate results for end-to-end learning for
monocular depth estimation. We also show preliminary results with a real
SaccadeCam hardware prototype.

本文提出了一种名为 SaccadeCam 的方法，通过自适应地分配分辨率到场景中的兴趣区域，实现单目深度估计的端到端学习。该算法将充分考虑物体在视觉系统中的感知特性。

SaccadeCam：自适应的视觉注意力在单目深度感知中的应用

SaccadeCam: Adaptive Visual Attention for Monocular Depth Sensing

In this work, we tackle the problem of ternary eye movement classification,
which aims to separate fixations, saccades and smooth pursuits from the raw eye
positional data. The efficient classification of these different types of eye
movements helps to better analyze and utilize the eye tracking data. Different
from the existing methods that detect eye movement by several pre-defined
threshold values, we propose a hierarchical Hidden Markov Model (HMM)
statistical algorithm for detecting fixations, saccades and smooth pursuits.
The proposed algorithm leverages different features from the recorded raw eye
tracking data with a hierarchical classification strategy, separating one type
of eye movement each time. Experimental results demonstrate the effectiveness
and robustness of the proposed method by achieving competitive or better
performance compared to the state-of-the-art methods.

本文提出了一种基于层次 HMM 算法的眼动分类方法，不同于现有的阈值方法，该算法通过纪录的原始眼动追踪数据中的不同特征进行分层分类，并逐一分离出一种眼动类型。实验结果表明，该方法在分离视线注视、扫视和平稳追踪时有较好的性能表现。