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DC poleHodnotaJazyk
dc.contributor.authorArad, Yoav
dc.contributor.authorWerman, Michael
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-25T19:25:25Z-
dc.date.available2024-07-25T19:25:25Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 33-46.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57375
dc.format14 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectdetekce anomálie videacs
dc.subjectpočítačové viděnícs
dc.subjectchytré sledovací systémycs
dc.titleBeyond the Benchmark: Detecting Diverse Anomalies in Videosen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedVideo Anomaly Detection (VAD) plays a crucial role in modern surveillance systems, aiming to identify various anomalies in real-world situations. However, current benchmark datasets predominantly emphasize simple, single frame anomalies such as novel object detection. This narrow focus restricts the advancement of VAD models. In this research, we advocate for an expansion of VAD investigations to encompass intricate anomalies that extend beyond conventional benchmark boundaries. To facilitate this, we introduce two datasets, HMDB-AD and HMDB Violence, to challenge models with diverse action-based anomalies. These datasets are derived from the HMDB51 action recognition dataset. We further present Multi-Frame Anomaly Detection (MFAD), a novel method built upon the AI-VAD framework. AI-VAD utilizes single-frame features such as pose estimation and deep image encoding, and two-frame features such as object velocity. They then apply a density estimation algorithm to com pute anomaly scores. To address complex multi-frame anomalies, we add deep video encoded features capturing long-range temporal dependencies, and logistic regression to enhance final score calculation. Experimental results confirm our assumptions, highlighting existing models limitations with new anomaly types. MFAD excels in both simple and complex anomaly detection scenarios.en
dc.subject.translatedvideo anomaly detectionen
dc.subject.translatedcomputer visionen
dc.subject.translatedsmart surveillance systemsen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.5
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:WSCG 2024: Full Papers Proceedings

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
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  4. WSCG: Full Papers
  5. WSCG 2024: Full Papers Proceedings