# Identifying First-person Camera Wearers in Third-person Videos

arXiv: https://arxiv.org/abs/1704.06340 · HTML: https://mlsanity.com/paper/1704.06340/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 18 of 18)

- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos](https://mlsanity.com/paper/2104.07905/) (2021)
- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [Retrieval-Augmented Egocentric Video Captioning](https://mlsanity.com/paper/2401.00789/) (2024)
- [Put Myself in Your Shoes: Lifting the Egocentric Perspective from Exocentric Videos](https://mlsanity.com/paper/2403.06351/) (2024)
- [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022)
- [Cross-View Exocentric to Egocentric Video Synthesis](https://mlsanity.com/paper/2107.03120/) (2021)
- [Intention-driven Ego-to-Exo Video Generation](https://mlsanity.com/paper/2403.09194/) (2024)
- [Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos](https://mlsanity.com/paper/2203.13309/) (2022)
- [Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision](https://mlsanity.com/paper/2506.06253/) (2025)
- [Egocentric and Exocentric Methods: A Short Survey](https://mlsanity.com/paper/2410.20621/) (2024)
- [Towards Comprehensive Scene Understanding: Integrating First and Third-Person Views for LVLMs](https://mlsanity.com/paper/2505.21955/) (2025)
- [Understanding Dynamic Scenes in Ego Centric 4D Point Clouds](https://mlsanity.com/paper/2508.07251/) (2025)
- [Enhancing Egocentric 3D Pose Estimation with Third Person Views](https://mlsanity.com/paper/2201.02017/) (2022)
- [VGGT-Segmentor: Geometry-Enhanced Cross-View Segmentation](https://mlsanity.com/paper/2604.13596/) (2026)
- [DOMR: Establishing Cross-View Segmentation via Dense Object Matching](https://mlsanity.com/paper/2508.04050/) (2025)
- [Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision](https://mlsanity.com/paper/2307.00064/) (2023)
- [Sequence-Based Identification of First-Person Camera Wearers in Third-Person Views](https://mlsanity.com/paper/2506.00394/) (2025)

## Abstract (arXiv)

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene. To do this, we need to establish person-level correspondences across first- and third-person videos, which is challenging because the camera wearer is not visible from his/her own egocentric video, preventing the use of direct feature matching. In this paper, we propose a new semi-Siamese Convolutional Neural Network architecture to address this novel challenge. We formulate the problem as learning a joint embedding space for first- and third-person videos that considers both spatial- and motion-domain cues. A new triplet loss function is designed to minimize the distance between correct first- and third-person matches while maximizing the distance between incorrect ones. This end-to-end approach performs significantly better than several baselines, in part by learning the first- and third-person features optimized for matching jointly with the distance measure itself.