Conversational group detection based on social context using graph clustering algorithm

With the development of single-person analysis in computer vision, social group analysis has received growing attention as the next area of research. In particular, group detection has been actively studied as the first step of social analysis. Here, group means an F-formation, that is, a spatial organization of people gathered for conversation. Popular group detection methods are based on coincidences in the visual attention field that are calculated from the position and body orientation of the individuals in the group. However, most previous studies have assumed that each member has the same visual attention field, and they do not consider changes in the scene over time. In this paper, we present a robust method for detection of time-varying F-formations in social space; its visual attention field model is based on the local environment. We present the results of an experiment that uses a dataset of multiple scenes; an analysis of these results validates the advantages of our method.