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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.

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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

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Additional Info

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