Automatic detection of interaction groups
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
CASSANDRA: audio-video sensor fusion for aggression detection
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Using social effects to guide tracking in complex scenes
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Modeling dominance in group conversations using nonverbal activity cues
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
It's not you, it's me: detecting flirting and its misperception in speed-dates
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
The idiap wolf corpus: exploring group behaviour in a competitive role-playing game
Proceedings of the international conference on Multimedia
Space speaks: towards socially and personality aware visual surveillance
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Group Level Activity Recognition in Crowded Environments across Multiple Cameras
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Cross-device interaction via micro-mobility and f-formations
Proceedings of the 25th annual ACM symposium on User interface software and technology
Social interaction detection using a multi-sensor approach
Proceedings of the 21st ACM international conference on Multimedia
Socially-Competent Computing Implementing Social Sensor Design
International Journal of Web-Based Learning and Teaching Technologies
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The first step towards analysing social interactive behaviour in crowded environments is to identify who is interacting with whom. This paper presents a new method for detecting focused encounters or F-formations in a crowded, real-life social environment. An F-formation is a specific instance of a group of people who are congregated together with the intent of conversing and exchanging information with each other. We propose a new method of estimating F-formations using a graph clustering algorithm by formulating the problem in terms of identifying dominant sets. A dominant set is a form of maximal clique which occurs in edge weighted graphs. As well as using the proximity between people, body orientation information is used; we propose a socially motivated estimate of focus orientation (SMEFO), which is calculated with location information only. Our experiments show significant improvements in performance over the existing modularity cut algorithm and indicates the effectiveness of using a local social context for detecting F-formations.