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
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An Appearance-Based Particle Filter for Visual Tracking in Smart Rooms
Multimodal Technologies for Perception of Humans
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Head Pose estimation on low resolution images
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Head pose estimation in seminar room using multi view face detectors
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Modeling focus of attention for meeting indexing based on multiple cues
IEEE Transactions on Neural Networks
Visual Focus of Attention in Dynamic Meeting Scenarios
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
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This paper presents a visual particle filter for jointly tracking the position of a person and her head pose. The resulting information may be used to support automatic analysis of interactive people behavior, by supporting proxemics analysis and providing dynamic information on focus of attention. A pose-sensitive visual likelihood is proposed which models the appearance of the target on a key-view basis, and uses body part color histograms as descriptors. Quantitative evaluations of the method on the `CLEAR'07 CHIL head pose' corpus are reported and discusssed. The integration of multi-view sensing, the joint estimation of location and orientation, the use of generative imaging models, and of simple visual matching measures, make the system robust to low image resolution and significant color distortion.