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Generalised Pose Estimation Using Depth

Hadfield, Simon and Bowden, Richard (2012) Generalised Pose Estimation Using Depth In: Trends and Topics in Computer Vision ECCV 2010. Lecture Notes in Computer Science, 6553 (6553). Springer, pp. 312-325.

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Abstract

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 isproposed requiring only a set of labelled training images in order to be applied to many pose estimation tasks. This is achieved bytreating 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: Book Section
Authors :
NameEmailORCID
Hadfield, Simons.hadfield@surrey.ac.ukUNSPECIFIED
Bowden, RichardR.Bowden@surrey.ac.ukUNSPECIFIED
Date : 2012
Identification Number : 10.1007/978-3-642-35749-7_24
Copyright Disclaimer : The final publication is available at link.springer.com
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/PBLSpringer, UNSPECIFIEDUNSPECIFIED
Related URLs :
Additional Information : Cite this paper as: Hadfield S., Bowden R. (2012) Generalised Pose Estimation Using Depth. In: Kutulakos K.N. (eds) Trends and Topics in Computer Vision. ECCV 2010. Lecture Notes in Computer Science, vol 6553. Springer, Berlin, Heidelberg
Depositing User : Symplectic Elements
Date Deposited : 07 Jul 2017 15:49
Last Modified : 07 Jul 2017 15:49
URI: http://epubs.surrey.ac.uk/id/eprint/808976

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