This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used. As being sparse, diverse, contextual, and often ambiguous, detecting abnormal events precisely is a very ambitious task. To this end, we rely only on the information-rich spatio-temporal data, and the reconstruction power of the diffusion models such that a high reconstruction error is utilized to decide the abnormality. Experiments performed on two large-scale video anomaly detection datasets demonstrate the consistent improvement of the proposed method over the state-of-the-art generative models while in some cases our method achieves better scores than the more complex models. This is the first study using a diffusion model and examining its parameters' influence to present guidance for VAD in surveillance scenarios.
Exploring Diffusion Models for Unsupervised Video Anomaly Detection / Osman Tur, Anil; Dall'Asen, Nicola; Beyan, Cigdem; Ricci, Elisa. - (2023), pp. 2540-2544. (Intervento presentato al convegno IEEE ICIP 2023 tenutosi a Kuala Lumpur, Malaysia nel 8th - 11th October, 2023) [10.1109/ICIP49359.2023.10222594].
Exploring Diffusion Models for Unsupervised Video Anomaly Detection
Osman Tur, Anil;Dall'Asen, Nicola;Beyan, Cigdem;Ricci, Elisa
2023-01-01
Abstract
This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used. As being sparse, diverse, contextual, and often ambiguous, detecting abnormal events precisely is a very ambitious task. To this end, we rely only on the information-rich spatio-temporal data, and the reconstruction power of the diffusion models such that a high reconstruction error is utilized to decide the abnormality. Experiments performed on two large-scale video anomaly detection datasets demonstrate the consistent improvement of the proposed method over the state-of-the-art generative models while in some cases our method achieves better scores than the more complex models. This is the first study using a diffusion model and examining its parameters' influence to present guidance for VAD in surveillance scenarios.File | Dimensione | Formato | |
---|---|---|---|
IC25_Exploring Diffusion Models for Unsupervised Video Anomaly Detection.pdf
accesso aperto
Tipologia:
Post-print referato (Refereed author’s manuscript)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
3.18 MB
Formato
Adobe PDF
|
3.18 MB | Adobe PDF | Visualizza/Apri |
Exploring_Diffusion_Models_for_Unsupervised_Video_Anomaly_Detection.pdf
Solo gestori archivio
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
3.88 MB
Formato
Adobe PDF
|
3.88 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione