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Video anomaly detection via prediction network with enhanced spatio-temporal memory exchange
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Shen, Guodong, Ouyang, Yuqi and Sanchez Silva, Victor (2022) Video anomaly detection via prediction network with enhanced spatio-temporal memory exchange. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 23-27 May 2022. Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3728-3732. ISBN 9781665405416. doi:10.1109/ICASSP43922.2022.9747376
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Official URL: http://doi.org/10.1109/ICASSP43922.2022.9747376
Abstract
Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not necessarily correspond to large reconstruction errors. To address this issue, we design a Convolutional LSTM Auto-Encoder prediction framework with enhanced spatiotemporal memory exchange using bi-directionalilty and a higher-order mechanism. The bi-directional structure promotes learning the temporal regularity through forward and backward predictions. The unique higher-order mechanism further strengthens spatial information interaction between the encoder and the decoder. Considering the limited receptive fields in Convolutional LSTMs, we also introduce an attention module to highlight informative features for prediction. Anomalies are eventually identified by comparing the frames with their corresponding predictions. Evaluations on three popular benchmarks show that our framework outperforms most existing prediction-based anomaly detection methods.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Anomaly detection (Computer security), Neural networks (Computer science), Machine learning | ||||||
Journal or Publication Title: | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | ||||||
Publisher: | IEEE Computer Society | ||||||
ISBN: | 9781665405416 | ||||||
Book Title: | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | ||||||
Official Date: | 27 April 2022 | ||||||
Dates: |
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Page Range: | pp. 3728-3732 | ||||||
DOI: | 10.1109/ICASSP43922.2022.9747376 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2022 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. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 1 July 2022 | ||||||
Date of first compliant Open Access: | 1 July 2022 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Singapore, Singapore | ||||||
Date(s) of Event: | 23-27 May 2022 |
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