Paper Info
Title
Conversational Group Detection Based on Social Context Using Graph Clustering Algorithm
Abstract
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.
Year
DOI
Venue
2016
10.1109/SITIS.2016.89
2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
Keywords
Field
DocType
conversational group detection,F-formation,graph clustering
Social group,Social environment,Computer vision,Conversation,Visualization,Computer science,Robustness (computer science),Spatial organization,Artificial intelligence,Cluster analysis,Clustering coefficient,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5090-5699-6
1
0.35
References 
Authors
11
2
Name
Order
Citations
PageRank
Shoichi Inaba110.35
Yoshimitsu Aoki28023.65