We envision a future time when wearable cameras are worn by the masses and
recording first-person point-of-view videos of everyday life. While these
cameras can enable new assistive technologies and novel research challenges,
they also raise serious privacy concerns. For example, first-person videos
passively recorded by wearable cameras will necessarily include anyone who
comes into the view of a camera -- with or without consent. Motivated by these
benefits and risks, we developed a self-search technique tailored to
first-person videos. The key observation of our work is that the egocentric
head motion of a target person (ie, the self) is observed both in the
point-of-view video of the target and observer. The motion correlation between
the target person's video and the observer's video can then be used to identify
instances of the self uniquely. We incorporate this feature into the proposed
approach that computes the motion correlation over densely-sampled trajectories
to search for a target individual in observer videos. Our approach
significantly improves self-search performance over several well-known face
detectors and recognizers. Furthermore, we show how our approach can enable
several practical applications such as privacy filtering, target video
retrieval, and social group clustering.

本文提出了一种基于自恋的头部运动的自我搜索技术，能够在可穿戴相机拍摄的第一人称视角视频中识别自己，以解决由这种技术带来的隐私问题，并且该技术能够应用于隐私过滤、目标视频检索以及社交群聚等实际场景中。