Ego-Surfing: Person Localization in First-Person Videos Using Ego-Motion Signatures

TL;DR

This paper introduces a novel method for locating a person in first-person videos by analyzing egocentric motion patterns, enhancing privacy filtering and social analysis capabilities.

Contribution

It presents a self-search technique leveraging egocentric motion correlation, outperforming face recognition methods in person localization within first-person videos.

Findings

Improved accuracy over face detectors and recognizers.

Enables privacy filtering and social group analysis.

Effective in diverse first-person video scenarios.

Abstract

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.