# Sequence-Based Identification of First-Person Camera Wearers in Third-Person Views

arXiv: https://arxiv.org/abs/2506.00394 · HTML: https://mlsanity.com/paper/2506.00394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 6 of 6)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Identifying First-person Camera Wearers in Third-person Videos](https://mlsanity.com/paper/1704.06340/) (2017)
- [Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos](https://mlsanity.com/paper/1803.11217/) (2018)
- [Ego2Top: Matching Viewers in Egocentric and Top-view Videos](https://mlsanity.com/paper/1607.06986/) (2016)
- [EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos](https://mlsanity.com/paper/1612.08153/) (2016)

## Abstract (arXiv)

The increasing popularity of egocentric cameras has generated growing interest in studying multi-camera interactions in shared environments. Although large-scale datasets such as Ego4D and Ego-Exo4D have propelled egocentric vision research, interactions between multiple camera wearers remain underexplored-a key gap for applications like immersive learning and collaborative robotics. To bridge this, we present TF2025, an expanded dataset with synchronized first- and third-person views. In addition, we introduce a sequence-based method to identify first-person wearers in third-person footage, combining motion cues and person re-identification.