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Object localisation via action recognition

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conference contribution
posted on 10.02.2016 by John Darby, Baihua Li, Ryan Cunningham, Nicholas Costen
The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified between action classes by a Support Vector Machine and converted to discriminative object pose hypotheses using a {body, object} pose mapping. They are then mixed with generative hypotheses by the importance sampler and evaluated against the image. The approach out-performs a state of the art adaptive tracker for localisation of 14/15 test implements and additionally gives object classifications and 3D object pose estimates.

History

School

  • Science

Department

  • Computer Science

Published in

Proceedings - International Conference on Pattern Recognition

Pages

817 - 820

Citation

DARBY, J. ... et al, 2012. Object localisation via action recognition. Proceedings - 21st International Conference on Pattern Recognition, 11th-15th November 2012, Tsukuba, pp.817-820

Publisher

IEEE (© ICPR2012 Organizing Committee)

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2012

Notes

This is the accepted manuscript version of the paper. IEEE (© ICPR2012 Organizing Committee). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9784990644109

ISSN

1051-4651

Language

en

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