Hogg, DC and Damen, D (2009) Recognising linked events: searching the space of feasible explanations. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. IEEE Computer Vision and Pattern Recognition , 20-15 Jun 2009, Miami, Florida, USA. IEEE , 927 - 934 . ISBN 978-1-4244-3991-1
Abstract
The ambiguity inherent in a localized analysis of events from video can be resolved by exploiting constraints between events and examining only feasible global explanations. We show how jointly recognizing and linking events can be formulated as labeling of a Bayesian network. The framework can be extended to multiple linking layers, expressing explanations as compositional hierarchies. The best global explanation is the Maximum a Posteriori (MAP) solution over a set of feasible explanations. The search space is sampled using Reversible Jump Markov Chain Monte Carlo (RJMCMC). We propose a set of general move types that is extensible to multiple layers of linkage, and use simulated annealing to find the MAP solution given all observations. We provide experimental results for a challenging two-layer linkage problem, demonstrating the ability to recognise and link drop and pick events of bicycles in a rack over five days.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2009, IEEE. This is an author produced version of a paper published in IEEE Conference on Computer Vision and Pattern Recognition, 2009. Uploaded 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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 May 2013 13:05 |
Last Modified: | 05 May 2023 09:42 |
Published Version: | http://dx.doi.org/10.1109/CVPR.2009.5206636 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CVPR.2009.5206636 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:75464 |