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Generalised Pose Estimation Using Depth

Hadfield, Simon J. and Bowden, Richard (2010) Generalised Pose Estimation Using Depth In: Workshop on Sign Gesture and Activity, 5 September 2010, Heraklion, Crete.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Hadfield, Simon
Date : 5 September 2010
Uncontrolled Keywords : Pose estimation, depth sensor, randomised forest, lbp, classification
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Additional Information : This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Depositing User : Simon Hadfield
Date Deposited : 07 Sep 2012 14:25
Last Modified : 24 Mar 2020 16:07

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