In this paper we propose a new framework to categorize social interactions in egocentric videos, we named InteractionGCN. Our method extracts patterns of relational and non-relational cues at the frame level and uses them to build a relational graph from which the interactional context at the frame level is estimated via a Graph Convolutional Network based approach. Then it propagates this context over time, together with first-person motion information, through a Gated Recurrent Unit architecture. Ablation studies and experimental evaluation on two publicly available datasets validate the proposed approach and establish state of the art results.
@article{arxiv.2104.14007,
title = {Interaction-GCN: A Graph Convolutional Network based framework for social interaction recognition in egocentric videos},
author = {Simone Felicioni and Mariella Dimiccoli},
journal= {arXiv preprint arXiv:2104.14007},
year = {2021}
}