Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Building a lightweight eyetracking headgear
Proceedings of the 2004 symposium on Eye tracking research & applications
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Variables Contributing to the Coordination of Rapid Eye/Head Gaze Shifts
Biological Cybernetics
Probabilistic Head Pose Tracking Evaluation in Single and Multiple Camera Setups
Multimodal Technologies for Perception of Humans
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Explorations in engagement for humans and robots
Artificial Intelligence
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
Models for multiparty engagement in open-world dialog
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Conditional sequence model for context-based recognition of gaze aversion
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Multiperson Visual Focus of Attention from Head Pose and Meeting Contextual Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Hi-index | 0.00 |
We address the recognition of people's visual focus of attention (VFOA), the discrete version of gaze that indicates who is looking at whom or what. As a good indicator of addressee-hood (who speaks to whom, and in particular is a person speaking to the robot) and of people's interest, VFOA is an important cue for supporting dialog modelling in Human-Robot interactions involving multiple persons. In absence of high definition images, we rely on people's head pose to recognize the VFOA. Rather than assuming a fixed mapping between head pose directions and gaze target directions, we investigate models that perform a dynamic (temporal) mapping implicitly accounting for varying body/shoulder orientations of a person over time, as well as unsupervised adaptation. Evaluated on a public dataset and on data recorded with the humanoid robot Nao, the method exhibit better adaptivity and versatility producing equal or better performance than a state-of-the-art approach, while the proposed unsupervised adaptation does not improve results.