Wang, H orcid.org/0000-0002-2281-5679 and O'Sullivan, C (2016) Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos. In: Computer Vision - ECCV 2016: Lecture Notes in Computer Science. European Conference on Computer Vision (ECCV) 2016, 08 Oct 2016, Amsterdam. Springer , pp. 527-544. ISBN 978-3-319-46454-1
Abstract
Automatically recognizing activities in video is a classic problem in vision and helps to understand behaviors, describe scenes and detect anomalies. We propose an unsupervised method for such purposes. Given video data, we discover recurring activity patterns that appear, peak, wane and disappear over time. By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge. To model the temporal changes of patterns, previous works compute Markovian progressions or locally continuous motifs whereas we model time in a globally continuous and non-Markovian way. Visually, the patterns depict flows of major activities. Temporally, each pattern has its own unique appearance-disappearance cycles. To compute compact pattern representations, we also propose a hybrid sampling method. By combining these patterns with detailed environment information, we interpret the semantics of activities and report anomalies. Also, our method fits data better and detects anomalies that were difficult to detect previously.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016, Springer International Publishing. This is an author produced version of a paper published in Lecture Notes in Computer Science. 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) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Oct 2016 10:42 |
Last Modified: | 04 Nov 2017 10:26 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-46454-1_32 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-319-46454-1_32 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106097 |