Tran, HTM and Hogg, D orcid.org/0000-0002-6125-9564 (2022) Anomaly Detection Using Prediction Error with Spatio-Temporal Convolutional LSTM. Journal of Science and Technology, 20 (6.2). pp. 7-12. ISSN 1859-1531
Abstract
In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-temporal convolutional Long Short-Term Memory (convLSTM). As in previous work on anomaly detection, anomalies arise as spatially localised failures in reconstruction or prediction. In experiments with five benchmark datasets, we show that using prediction gives superior performance to using reconstruction. We also compare performance with different length input/output sequences. Overall, our results using prediction are comparable with the state of the art on the benchmark datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Convolutional LSTM; convolutional autoencoder; prediction error; reconstruction error; anomaly detection |
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: | 27 Mar 2023 13:55 |
Last Modified: | 27 Mar 2023 13:55 |
Status: | Published |
Publisher: | The University of Danang |
Identification Number: | 10.31130/ud-jst.2022.289e |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197715 |