Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analyzing and predicting focus of attention in remote collaborative tasks
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Extracting information from multimedia meeting collections
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Where's the "party" in "multi-party"?: analyzing the structure of small-group sociable talk
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Proceedings of the 8th international conference on Multimodal interfaces
Tracking head pose and focus of attention with multiple far-field cameras
Proceedings of the 8th international conference on Multimodal interfaces
Sharing a single expert among multiple partners
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Graphical representation of meetings on mobile devices
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Joint Bayesian Tracking of Head Location and Pose from Low-Resolution Video
Multimodal Technologies for Perception of Humans
Fast and Robust Face Tracking for Analyzing Multiparty Face-to-Face Meetings
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Visual information as a conversational resource in collaborative physical tasks
Human-Computer Interaction
Image and Vision Computing
Automatic nonverbal analysis of social interaction in small groups: A review
Image and Vision Computing
Communicative gestures in coreference identification in multiparty meetings
Proceedings of the 2009 international conference on Multimodal interfaces
Recognizing visual focus of attention from head pose in natural meetings
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Three-dimensional face pose detection and tracking using monocular videos: tool and application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Smart meeting systems: A survey of state-of-the-art and open issues
ACM Computing Surveys (CSUR)
Visual activity context for focus of attention estimation in dynamic meetings
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Head pose tracking and focus of attention recognition algorithms in meeting rooms
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Unsupervised clustering in multimodal multiparty meeting analysis
Multimodal corpora
IEEE Transactions on Intelligent Transportation Systems
Putting the pieces together: multimodal analysis of social attention in meetings
Proceedings of the international conference on Multimedia
3D head pose estimation and tracking using particle filtering and ICP algorithm
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Employing social gaze and speaking activity for automatic determination of the Extraversion trait
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Ambient Suite: enhancing communication among multiple participants
Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology
Comparative assessment of content-based face image retrieval in different color spaces
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
A study on visual focus of attention recognition from head pose in a meeting room
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
Proceedings of the 15th ACM on International conference on multimodal interaction
Leveraging the robot dialog state for visual focus of attention recognition
Proceedings of the 15th ACM on International conference on multimodal interaction
Context aware addressee estimation for human robot interaction
Proceedings of the 6th workshop on Eye gaze in intelligent human machine interaction: gaze in multimodal interaction
Overt or subtle? Supporting group conversations with automatically targeted directives
Proceedings of the 19th international conference on Intelligent User Interfaces
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A user's focus of attention plays an important role in human-computer interaction applications, such as a ubiquitous computing environment and intelligent space, where the user's goal and intent have to be continuously monitored. We are interested in modeling people's focus of attention in a meeting situation. We propose to model participants' focus of attention from multiple cues. We have developed a system to estimate participants' focus of attention from gaze directions and sound sources. We employ an omnidirectional camera to simultaneously track participants' faces around a meeting table and use neural networks to estimate their head poses. In addition, we use microphones to detect who is speaking. The system predicts participants' focus of attention from acoustic and visual information separately. The system then combines the output of the audio- and video-based focus of attention predictors. We have evaluated the system using the data from three recorded meetings. The acoustic information has provided 8% relative error reduction on average compared to only using one modality. The focus of attention model can be used as an index for a multimedia meeting record. It can also be used for analyzing a meeting.