Greenall, J, Hogg, DC and Cohn, AG (2011) Temporal Structure Models for Event Recognition. Proceedings of the British Machine Vision Conference, pages. 62.1 - 62.11.
Abstract
In many areas of visual surveillance, the observed activity follows re-occurring patterns. This paper demonstrates how such patterns can be exploited to improve the detection rate of independent event detectors. We present a temporal model based on pairwise correlations between event timings, which efficiently exploits limited training data. This is combined with the response from potentially heterogeneous independent event detectors to improve the robustness of detections over extended sequences. We demonstrate the efficacy of our system with rigorous testing on a large real-world dataset of aircraft servicing operations. We describe the implementation of a binary classifier based on local histograms of optical flow which is used as the independent event detector in our experiments.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2011, Greenall et al. Reproduced in accordance with the publisher's self-archiving policy. |
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: | 29 Apr 2013 15:42 |
Last Modified: | 29 Mar 2018 17:31 |
Published Version: | http://dx.doi.org/10.5244/C.25 |
Status: | Published |
Publisher: | BMVA Press |
Identification Number: | 10.5244/C.25 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:75463 |