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Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation

Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances.

Data and Resources

Cite this as

Ayush K. Rai, Tarun Krishna, Feiyan Hu, Alexandru Drimbarean, Kevin McGuinness, Alan F. Smeaton, Noel E. O’Connor (2024). Dataset: Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation. https://doi.org/10.57702/1e8db4rz

DOI retrieved: December 2, 2024

Additional Info

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Created December 2, 2024
Last update December 2, 2024
Author Ayush K. Rai
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Tarun Krishna
Feiyan Hu
Alexandru Drimbarean
Kevin McGuinness
Alan F. Smeaton
Noel E. O’Connor
Homepage https://github.com/aseuteurideu/LearningNotToReconstructAnomalies