| Paper ID | ARS-3.11 | ||
| Paper Title | INTERACTION-GCN: A GRAPH CONVOLUTIONAL NETWORK BASED FRAMEWORK FOR SOCIAL INTERACTION RECOGNITION IN EGOCENTRIC VIDEOS | ||
| Authors | Simone Felicioni, University of Perugia, Italy; Mariella Dimiccoli, Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Spain | ||
| Session | ARS-3: Image and Video Biometric Analysis | ||
| Location | Area H | ||
| Session Time: | Monday, 20 September, 13:30 - 15:00 | ||
| Presentation Time: | Monday, 20 September, 13:30 - 15:00 | ||
| Presentation | Poster | ||
| Topic | Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| 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. | ||