Plans and situated actions: the problem of human-machine communication
Plans and situated actions: the problem of human-machine communication
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart Office: Design of an Intelligent Environment
IEEE Intelligent Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Conversational scene analysis
Learning to Detect User Activity and Availability from a Variety of Sensor Data
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Multimodal group action clustering in meetings
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic detection of interaction groups
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment
Presence: Teleoperators and Virtual Environments
Semi-Markov conditional random fields for accelerometer-based activity recognition
Applied Intelligence
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Mining bridging rules between conceptual clusters
Applied Intelligence
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This article addresses the problem of detecting configurations and activities of small groups of people in an augmented environment. The proposed approach takes a continuous stream of observations coming from different sensors in the environment as input. The goal is to separate distinct distributions of these observations corresponding to distinct group configurations and activities. This article describes an unsupervised method based on the calculation of the Jeffrey divergence between histograms over observations. These histograms are generated from adjacent windows of variable size slid from the beginning to the end of a meeting recording. The peaks of the resulting Jeffrey divergence curves are detected using successive robust mean estimation. After a merging and filtering process, the retained peaks are used to select the best model, i.e. the best allocation of observation distributions for a meeting recording. These distinct distributions can be interpreted as distinct segments of group configuration and activity. To evaluate this approach, 5 small group meetings, one seminar and one cocktail party meeting have been recorded. The observations of the small groups meetings and the seminar were generated by a speech activity detector, while the observations of the cocktail party meeting were generated by both the speech activity detector and a visual tracking system. The authors measured the correspondence between detected segments and labeled group configurations and activities. The obtained results are promising, in particular as the method is completely unsupervised.