Behera, A, Cohn, AG and Hogg, DC (2014) Real-time activity recognition by discerning qualitative relationships between randomly chosen visual features. In: Valstar, M, French, A and Pridmore, T, (eds.) BMVC 2014 - Proceedings of the British Machine Vision Conference 2014. British Machine Vision Conference 2014, 01-05 Sep 2014, Nottingham, UK. British Machine Vision Association, BMVA
Abstract
In this paper, we present a novel method to explore semantically meaningful visual information and identify the discriminative spatiotemporal relationships between them for real-time activity recognition. Our approach infers human activities using continuous egocentric (first-person-view) videos of object manipulations in an industrial setup. In order to achieve this goal, we propose a random forest that unifies randomization, discriminative relationships mining and a Markov temporal structure. Discriminative relationships mining helps us to model relations that distinguish different activities, while randomization allows us to handle the large feature space and prevents over-fitting. The Markov temporal structure provides temporally consistent decisions during testing. The proposed random forest uses a discriminative Markov decision tree, where every nonterminal node is a discriminative classifier and the Markov structure is applied at leaf nodes. The proposed approach outperforms the state-of-the-art methods on a new challenging video dataset of assembling a pump system.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | (c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. |
Dates: |
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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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Mar 2015 14:31 |
Last Modified: | 21 Feb 2024 14:05 |
Published Version: | http://www.bmva.org/bmvc/2014/ |
Status: | Published |
Publisher: | British Machine Vision Association, BMVA |
Identification Number: | 10.5244/c.28.100 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83878 |