Detection of free-standing conversational groups with graph convolutional networks

Abstract

Automatically detecting conversational groups from video footage is an important research area with applications in social robotics, human-computer interaction, and video analysis. In this paper, we use a novel approach based on Graph Convolutional Networks (GCN) for the social group detection problem. We base our approach on a method from the community detection domain called Deep Modularity Networks (DMoN). As an input to the DMoN method, we construct a graph representation using body view frustums, which indicates affinities among the individuals. In addition to the frame-level group detector, the proposed system utilizes temporal information to improve the group detection accuracy. Experiments conducted on the standard benchmark dataset SALSA confirm that our GCN-based approach improves the group detection quality over state-of-the-art group detection methods.