CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regression-based Hand Pose Estimation from Multiple Cameras
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time Hand Pose Recognition Using Low-Resolution Depth Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Visual gaze projection in front of a target scene
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Robust real-time 3D head pose estimation from range data
Pattern Recognition
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Hand posture recognition from disparity cost map
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Estimating the pose of an object, be it articulated, deformable or rigid, is an important task, with applications ranging from Human-Computer Interaction to environmental understanding. The idea of a general pose estimation framework, capable of being rapidly retrained to suit a variety of tasks, is appealing. In this paper a solution is proposed requiring only a set of labelled training images in order to be applied to many pose estimation tasks. This is achieved by treating pose estimation as a classification problem, with particle filtering used to provide non-discretised estimates. Depth information extracted from a calibrated stereo sequence, is used for background suppression and object scale estimation. The appearance and shape channels are then transformed to Local Binary Pattern histograms, and pose classification is performed via a randomised decision forest. To demonstrate flexibility, the approach is applied to two different situations, articulated hand pose and rigid head orientation, achieving 97% and 84% accurate estimation rates, respectively.