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

Publication

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

Conference Article

Conference

IEEE International Conference on Image Processing (ICIP)

Edition

28th

Pages

2348-2352

Doc link

https://doi.org/10.1109/ICIP42928.2021.9506690

File

Download the digital copy of the doc pdf document

Authors

Projects associated

SPECTACLE: From Spectators to Actors: Understanding Social Interactions through the Eyes of a Participant

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.

Scientific reference

S. Felicioni and M. Dimiccoli. Interaction-GCN: a Graph Convolutional Network based framework for social interaction recognition in egocentric videos, 28th IEEE International Conference on Image Processing, 2021, Anchorage, AK, USA, pp. 2348-2352.