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dc.date.accessioned2023-08-11T16:59:11Z
dc.date.available2023-08-11T16:59:11Z
dc.date.created2023-03-12T13:18:02Z
dc.date.issued2023
dc.identifier.citationMonakhov, Vladimir Thambawita, Vajira L B Halvorsen, Pål Riegler, Michael . GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos. Sensors. 2023, 23:2087(4), 1-15
dc.identifier.urihttp://hdl.handle.net/10852/103175
dc.description.abstractThe interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleGridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos
dc.title.alternativeENEngelskEnglishGridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos
dc.typeJournal article
dc.creator.authorMonakhov, Vladimir
dc.creator.authorThambawita, Vajira L B
dc.creator.authorHalvorsen, Pål
dc.creator.authorRiegler, Michael
cristin.unitcode185,15,5,0
cristin.unitnameInstitutt for informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin2133278
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Sensors&rft.volume=23:2087&rft.spage=1&rft.date=2023
dc.identifier.jtitleSensors
dc.identifier.volume23
dc.identifier.issue4
dc.identifier.doihttps://doi.org/10.3390/s23042087
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1424-8220
dc.type.versionPublishedVersion
cristin.articleid2087


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