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Authors: Brian Holt and Richard Bowden

Affiliation: University of Surrey, United Kingdom

ISBN: 978-989-8565-03-7

Keyword(s): Pose Estimation, Human Body, Regression Forests, Range Image, Hough Transform, Kinect.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Enterprise Information Systems ; Human and Computer Interaction ; Human-Computer Interaction

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-of-the-art on a publicly available dataset.

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Paper citation in several formats:
Holt B. and Bowden R. (2012). STATIC POSE ESTIMATION FROM DEPTH IMAGES USING RANDOM REGRESSION FORESTS AND HOUGH VOTING.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 557-564. DOI: 10.5220/0003868005570564

@conference{visapp12,
author={Brian Holt and Richard Bowden},
title={STATIC POSE ESTIMATION FROM DEPTH IMAGES USING RANDOM REGRESSION FORESTS AND HOUGH VOTING},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={557-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003868005570564},
isbn={978-989-8565-03-7},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - STATIC POSE ESTIMATION FROM DEPTH IMAGES USING RANDOM REGRESSION FORESTS AND HOUGH VOTING
SN - 978-989-8565-03-7
AU - Holt B.
AU - Bowden R.
PY - 2012
SP - 557
EP - 564
DO - 10.5220/0003868005570564

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