Tayyub, J, Hawasly, M orcid.org/0000-0003-1823-5580, Hogg, DC orcid.org/0000-0002-6125-9564 et al. (1 more author) (2018) Learning Hierarchical Models of Complex Daily Activities from Annotated Videos. In: Applications of Computer Vision (WACV), 2018 IEEE Winter Conference on. 2018 IEEE Winter Conference on Applications of Computer Vision, 12-15 Mar 2018, Lake Tahoe, Nevada, USA. IEEE , pp. 1633-1641. ISBN 978-1-5386-4886-5
Abstract
Effective recognition of complex long-term activities is becoming an increasingly important task in artificial intelligence. In this paper, we propose a novel approach for building models of complex long-term activities. First, we automatically learn the hierarchical structure of activities by learning about the 'parent-child' relation of activity components from a video using the variability in annotations acquired using multiple annotators. This variability allows for extracting the inherent hierarchical structure of the activity in a video. We consolidate hierarchical structures of the same activity from different videos into a unified stochastic grammar describing the overall activity. We then describe an inference mechanism to interpret new instances of activities. We use three datasets, which have been annotated by multiple annotators, of daily activity videos to demonstrate the effectiveness of our system.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018, IEEE. 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. |
Keywords: | Hidden Markov models; Videos; Semantics; Grammar; Activity recognition; Training; Stochastic processes |
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) |
Funding Information: | Funder Grant number EU - European Union FP7-ICT-600623 |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Mar 2018 12:51 |
Last Modified: | 16 Dec 2024 09:45 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/WACV.2018.00182 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128307 |