title: Group interaction analysis in dynamic context.
abstract: Computer understanding of human actions and interactions is one of the key research issues in human computing. In this regard, context plays an essential role in semantic understanding of human behavioral and social signals from sensor data. This paper put forward an event-based dynamic context model to address the problems of context awareness in the analysis of group interaction scenarios. Event-driven multilevel dynamic Bayesian network is correspondingly proposed to detect multilevel events, which underlies the context awareness mechanism. Online analysis can be achieved, which is superior over previous works. Experiments in our smart meeting room demonstrate the effectiveness of our approach.
authors.forename: Peng
authors.initials: P
authors.lastname: Dai
authors.forename: Huijun
authors.initials: H
authors.lastname: Di
authors.forename: Ligeng
authors.initials: L
authors.lastname: Dong
authors.forename: Linmi
authors.initials: L
authors.lastname: Tao
authors.forename: Guangyou
authors.initials: G
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003142:Communication; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011157:Population Dynamics; D012919:Social Behavior