Computing 3-D head orientation from a monocular image sequence
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
A model-based gaze tracking system
IJSIS '96 Proceedings of the 1996 IEEE International Joint Symposia on Intelligence and Systems
Simultaneous Tracking of Head Poses in a Panoramic View
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Absolute Head Pose Estimation From Overhead Wide-Angle Cameras
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A Probabilistic Framework for Joint Head Tracking and Pose Estimation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Multi-View Head Pose Estimation using Neural Networks
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
A joint particle filter for audio-visual speaker tracking
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Tracking the multi person wandering visual focus of attention
Proceedings of the 8th international conference on Multimodal interfaces
Pose estimation from multiple cameras based on Sylvester's equation
Computer Vision and Image Understanding
Analysis environment of conversational structure with nonverbal multimodal data
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
The connector service-predicting availability in mobile contexts
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
| Hi-index | 0.00 |
In this paper, we present a system to track the horizontal head orientation of a lecturer in a smart seminar room, which is equipped with several cameras. We automatically detect and track the face of the lecturer and use neural networks to classify his or her face orientation in each camera view. By combining the single estimates of the speaker's head orientation from multiple cameras into one joint hypothesis, we improve overall head pose estimation accuracy. We conducted experiments on annotated recordings from real seminars. Using the proposed fully automatic system we are able to correctly determine the lecturer's head pose in 59% of the time and for 8 orientation classes. In 92% of the time, the correct pose class or a neighbouring pose class (i.e. a 45 degree error) were estimated.