article

Dynamic video anomaly detection and localization using sparse denoising autoencoders

Authors:

Medhini G. Narasimhan,

Sowmya Kamath S.Authors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 11

Pages 13173 - 13195

https://doi.org/10.1007/s11042-017-4940-2

Published: 01 June 2018 Publication History

Abstract

The emergence of novel techniques for automatic anomaly detection in surveillance videos has significantly reduced the burden of manual processing of large, continuous video streams. However, existing anomaly detection systems suffer from a high false-positive rate and also, are not real-time, which makes them practically redundant. Furthermore, their predefined feature selection techniques limit their application to specific cases. To overcome these shortcomings, a dynamic anomaly detection and localization system is proposed, which uses deep learning to automatically learn relevant features. In this technique, each video is represented as a group of cubic patches for identifying local and global anomalies. A unique sparse denoising autoencoder architecture is used, that significantly reduced the computation time and the number of false positives in frame-level anomaly detection by more than 2.5%. Experimental analysis on two benchmark data sets - UMN dataset and UCSD Pedestrian dataset, show that our algorithm outperforms the state-of-the-art models in terms of false positive rate, while also showing a significant reduction in computation time.

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Cited By

Mumtaz ASargano AHabib Z(2024)AnomalyNet: a spatiotemporal motion-aware CNN approach for detecting anomalies in real-world autonomous surveillanceThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03210-440:11(7823-7844)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00371-023-03210-4
Kotkar VSucharita V(2023)Fast anomaly detection in video surveillance system using robust spatiotemporal and deep learning methodsMultimedia Tools and Applications10.1007/s11042-023-14840-082:22(34259-34286)Online publication date: 4-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14840-0
Mumtaz ASargano AHabib Z(2023)Robust learning for real-world anomalies in surveillance videosMultimedia Tools and Applications10.1007/s11042-023-14425-x82:13(20303-20322)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1007/s11042-023-14425-x
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Dynamic video anomaly detection and localization using sparse denoising autoencoders
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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 11

June 2018

1406 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2018

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Citations

Cited By

Mumtaz ASargano AHabib Z(2024)AnomalyNet: a spatiotemporal motion-aware CNN approach for detecting anomalies in real-world autonomous surveillanceThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03210-440:11(7823-7844)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00371-023-03210-4
Kotkar VSucharita V(2023)Fast anomaly detection in video surveillance system using robust spatiotemporal and deep learning methodsMultimedia Tools and Applications10.1007/s11042-023-14840-082:22(34259-34286)Online publication date: 4-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14840-0
Mumtaz ASargano AHabib Z(2023)Robust learning for real-world anomalies in surveillance videosMultimedia Tools and Applications10.1007/s11042-023-14425-x82:13(20303-20322)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1007/s11042-023-14425-x
Zhang Z(2022)Detecting Anomaly Event in Video Based on Generative Adversarial NetworkComputational Intelligence and Neuroscience10.1155/2022/86339552022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8633955
Tran TVu TVo NNguyen TNguyen K(2022)Anomaly Analysis in Images and Videos: A Comprehensive ReviewACM Computing Surveys10.1145/354401455:7(1-37)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3544014
Zhao YMan KSmith JGuan S(2022)A novel two-stream structure for video anomaly detection in smart city managementThe Journal of Supercomputing10.1007/s11227-021-04007-978:3(3940-3954)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11227-021-04007-9
Fernández-Saúco IAcosta-Mendoza NGago-Alonso AGarcía-Reyes E(2018)Computing Anomaly Score Threshold with Autoencoders PipelineProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-13469-3_28(237-244)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-13469-3_28

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