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UCF-Crime

Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with either video-level supervision, one-class supervision, or in an unsupervised setting.

Data and Resources

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Cite this as

Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang, Elisa Ricci (2025). Dataset: UCF-Crime. https://doi.org/10.57702/gy6tc627

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It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field	Value
Created	January 2, 2025
Last update	January 2, 2025
Defined In	https://doi.org/10.48550/arXiv.2404.01014
Author	Luca Zanella
More Authors	Willi Menapace Massimiliano Mancini Yiming Wang Elisa Ricci
Homepage	https://lucazanella.github.io/lavad/