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Interaction-GCN: a Graph Convolutional Network based framework for social interaction recognition in egocentric videos

2021-04-28 20:25:40
Simone Felicioni, Mariella Dimiccoli
arXiv_CV
             
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2104.14007

PDF

https://arxiv.org/pdf/2104.14007.pdf

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