Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
A quantitative analysis for decomposing visual signal of the gaze displacement
VIP '01 Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11
Speaker localization for microphone array-based ASR: the effects of accuracy on overlapping speech
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
Audio-visual multi-person tracking and identification for smart environments
Proceedings of the 15th international conference on Multimedia
An Appearance-Based Particle Filter for Visual Tracking in Smart Rooms
Multimodal Technologies for Perception of Humans
Joint Bayesian Tracking of Head Location and Pose from Low-Resolution Video
Multimodal Technologies for Perception of Humans
Head Pose Estimation in Single- and Multi-view Environments - Results on the CLEAR'07 Benchmarks
Multimodal Technologies for Perception of Humans
Face recognition in smart rooms
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
A multimodal analysis of floor control in meetings
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Estimating human body and head orientation change to detect visual attention direction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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This paper presents our data collection and first evaluations on visual focus of attention during dynamic meeting scenes. We included moving focus targets and unforeseen interruptions in each meeting, by guiding each meeting along a predefined script of events that three participating actors were instructed to follow. Further meeting attendees were not introduced to upcoming actions or the general purpose of the meeting, hence we were able to capture their natural focus changes within this predefined dynamic scenario with an extensive setup of both visual and acoustical sensors throughout our smart room. We present an adaptive approach to estimate visual focus of attention based on head orientation under these unforeseen conditions and show, that our system achieves an overall recognition rate of 59%, compared to 9% less when choosing the best matching focus target directly from the observed head orientation angles.