ICPR2012-CR.pdf (1.64 MB)
Object localisation via action recognition
conference contribution
posted on 2016-02-10, 11:35 authored by John Darby, Baihua LiBaihua Li, Ryan Cunningham, Nicholas CostenThe 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 RecognitionPages
817 - 820Citation
DARBY, J. ... et al, 2012. Object localisation via action recognition. Proceedings - 21st International Conference on Pattern Recognition, 11th-15th November 2012, Tsukuba, pp.817-820Publisher
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
2012Notes
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
9784990644109ISSN
1051-4651Publisher version
Language
- en