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Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset’ness of anomalies. But normalcy shares the same openset’ness property since humans can perform the same action in several ways, which the leading techniques neglect.

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Alessandro Flaborea, Luca Collorone, Guido Maria D’Amely di Melendugno, Stefano D’Arrigo, Bardh Prenkaj, Fabio Galasso (2024). Dataset: Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection. https://doi.org/10.57702/pr0y2b9t

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Additional Info

Field Value
Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2307.07205
Author Alessandro Flaborea
More Authors
Luca Collorone
Guido Maria D’Amely di Melendugno
Stefano D’Arrigo
Bardh Prenkaj
Fabio Galasso
Homepage https://github.com/aleflabo/MoCoDAD