Exploring Diffusion Models for Unsupervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2304.05841v2
• Date: Sun, 2 Jul 2023 08:55:34 GMT
• Title: Exploring Diffusion Models for Unsupervised Video Anomaly Detection
• Authors: Anil Osman Tur and Nicola Dall'Asen and Cigdem Beyan and Elisa Ricci
• Abstract summary: This paper investigates the performance of diffusion models for video anomaly detection (VAD) Experiments performed on two large-scale anomaly detection datasets demonstrate the consistent improvement of the proposed method over the state-of-the-art generative models. This is the first study using a diffusion model to present guidance for examining VAD in surveillance scenarios.
• Score: 17.816344808780965
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multivariate Time Series Anomaly Detection using DiffGAN Model [6.62154383680049]
  We propose DiffGAN, which adds a generative adversarial network component to the denoiser of diffusion model. Compared to multiple state-of-the-art reconstruction models, experimental results demonstrate that DiffGAN achieves superior performance in anomaly detection.
  arXiv  Detail & Related papers  (2025-01-03T01:20:38Z)
• Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model [15.67669626517481]
  We propose a novel patch-based diffusion model for video anomaly detection. We introduce innovative motion and appearance conditions that are seamlessly integrated into our model. Experimental results in four challenging video anomaly detection datasets substantiate the efficacy of our proposed approach.
  arXiv  Detail & Related papers  (2024-12-12T07:42:50Z)
• Anomaly detection using Diffusion-based methods [15.049468347670421]
  This paper explores the utility of diffusion-based models for anomaly detection. It focuses on their efficacy in identifying deviations in both compact and high-resolution datasets.
  arXiv  Detail & Related papers  (2024-12-10T14:17:23Z)
• GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [60.78684630040313]
  Diffusion models tend to reconstruct normal counterparts of test images with certain noises added. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. We propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2024-06-11T17:27:23Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations [17.816344808780965]
  unsupervised video anomaly detection (VAD) problem involves classifying each frame in a video as normal or abnormal, without any access to labels. To accomplish this, proposed method employs conditional diffusion models, where the input data is features extracted from pre-trained network. Our method utilizes a data-driven threshold and considers a high reconstruction error as an indicator of anomalous events.
  arXiv  Detail & Related papers  (2023-07-04T07:36:48Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.