Static pose estimation from depth images using random regression forests and Hough voting

Holt, B and Bowden, R (2012) Static pose estimation from depth images using random regression forests and Hough voting In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2012-02-24 - 2012-02-26, Rome. (Unpublished)

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Abstract

Robust and fast algorithms for estimating the pose of a human given an image would have a far reaching impact on many fields in and outside of computer vision. We address the problem using depth data that can be captured inexpensively using consumer depth cameras such as the Kinect sensor. To achieve robustness and speed on a small training dataset, we formulate the pose estimation task within a regression and Hough voting framework. Our approach uses random regression forests to predict joint locations from each pixel and accumulate these predictions with Hough voting. The Hough accumulator images are treated as likelihood distributions where maxima correspond to joint location hypotheses. We demonstrate our approach and compare to the state-ofthe-art on a publicly available dataset.

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 :	Name Email ORCID Holt, B Bowden, R
Date :	February 2012
Additional Information :	This is the author's version of the paper.
Depositing User :	Symplectic Elements
Date Deposited :	17 Nov 2015 18:52
Last Modified :	31 Oct 2017 17:46
URI:	http://epubs.surrey.ac.uk/id/eprint/808978

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