Tayyub, J, Tavanai, A, Gatsoulis, Y et al. (2 more authors) (2015) Qualitative and quantitative spatio-temporal relations in daily living activity recognition. In: Cremers, D, Reid, I, Saito, H and Yang, M-H, (eds.) Computer Vision -- ACCV 2014 12th Asian Conference on Computer Vision 2014, Revised Selected Papers, Part V. 12th Asian Conference on Computer Vision, 01-05 Nov 2014, Singapore. Lecture Notes in Computer Science . Springer Verlag , 115 - 130. ISBN 9783319168135
Abstract
For the effective operation of intelligent assistive systems working in real-world human environments, it is important to be able to recognise human activities and their intentions. In this paper we propose a novel approach to activity recognition from visual data. Our approach is based on qualitative and quantitative spatio-temporal features which encode the interactions between human subjects and objects in an efficient manner. Unlike the state of the art, our approach uses significantly fewer assumptions and does not require knowledge about object types, their affordances, or the sub-level activities that high-level activities consist of. We perform an automatic feature selection process which provides the most representative descriptions of the learnt activities. We validated the method using these descriptions on the CAD-120 benchmark dataset, consisting of video sequences showing humans performing daily real-world activities. The method is shown to outperform state of the art benchmarks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Springer. This is an author produced version of a paper subsequently published in Lecture notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16814-2_8 |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Funding Information: | Funder Grant number EU - European Union 287752 EU - European Union FP7-ICT-600623 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Oct 2015 11:28 |
Last Modified: | 19 Dec 2022 13:31 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-16814-2_8 |
Status: | Published |
Publisher: | Springer Verlag |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-319-16814-2_8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:88923 |