In a wearable camera video, we see what the camera wearer sees. While this
makes it easy to know roughly what he chose to look at, it does not immediately
reveal when he was engaged with the environment. Specifically, at what moments
did his focus linger, as he paused to gather more information about something
he saw? Knowing this answer would benefit various applications in video
summarization and augmented reality, yet prior work focuses solely on the
"what" question (estimating saliency, gaze) without considering the "when"
(engagement). We propose a learning-based approach that uses long-term
egomotion cues to detect engagement, specifically in browsing scenarios where
one frequently takes in new visual information (e.g., shopping, touring). We
introduce a large, richly annotated dataset for ego-engagement that is the
first of its kind. Our approach outperforms a wide array of existing methods.
We show engagement can be detected well independent of both scene appearance
and the camera wearer's identity.

该研究提出了一种基于长期自我运动线索的学习方法来检测佩戴者在穿越场景时的注意力，特别是在需要获取新的视觉信息时，与以前的研究相比，该方法可以独立于场景外观和佩戴者身份来有效地检测 engagement。