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

Cite this as

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

DOI retrieved: January 2, 2025

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/