Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking the multi person wandering visual focus of attention
Proceedings of the 8th international conference on Multimodal interfaces
Proceedings of the 9th international conference on Multimodal interfaces
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Monitoring visual focus of attention via local discriminant projection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Ambient kitchen: designing situated services using a high fidelity prototyping environment
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers
AmI '09 Proceedings of the European Conference on Ambient 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
Hi-index | 0.00 |
This paper presents a model for visual focus of attention recognition in the Ambient Kitchen, a pervasive computing prototyping environment The kitchen is equipped with several blended displays on one wall and users may use information presented on these displays from multiple locations Our goal is to recognize which display the user is looking at so that the environment can adjust the display content accordingly We propose a dynamic Bayesian network model to infer the focus of attention, which models the relation between multiple foci of attention, multiple user locations and faces captured by the multiple cameras in the environment Head pose is not explicitly computed but measured by a similarity vector which represents the likelihoods of multiple face clusters Video data are collected in the Ambient Kitchen environment and experimental results demonstrate the effectiveness of our model.