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Paper Detail

Paper IDARS-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
SessionARS-3: Image and Video Biometric Analysis
LocationArea 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.