Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model

• URL: http://arxiv.org/abs/2412.09026v1
• Date: Thu, 12 Dec 2024 07:42:50 GMT
• Title: Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model
• Authors: Hang Zhou, Jiale Cai, Yuteng Ye, Yonghui Feng, Chenxing Gao, Junqing Yu, Zikai Song, Wei Yang,
• Abstract summary: 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.
• Score: 15.67669626517481
• License:
• Abstract: A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recover normal patterns exclusively, thus reporting abnormal patterns as outliers. Yet, existing attempts neglect the various formations of anomaly and predict normal samples at the feature level regardless that abnormal objects in surveillance videos are often relatively small. To address this, a novel patch-based diffusion model is proposed, specifically engineered to capture fine-grained local information. We further observe that anomalies in videos manifest themselves as deviations in both appearance and motion. Therefore, we argue that a comprehensive solution must consider both of these aspects simultaneously to achieve accurate frame prediction. To address this, we introduce innovative motion and appearance conditions that are seamlessly integrated into our patch diffusion model. These conditions are designed to guide the model in generating coherent and contextually appropriate predictions for both semantic content and motion relations. Experimental results in four challenging video anomaly detection datasets empirically substantiate the efficacy of our proposed approach, demonstrating that it consistently outperforms most existing methods in detecting abnormal behaviors.

Related papers

• 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)
• Exploring Diffusion Models for Unsupervised Video Anomaly Detection [17.816344808780965]
  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.
  arXiv  Detail & Related papers  (2023-04-12T13:16:07Z)
• A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency [18.06814233420315]
  We propose a framework that uses normal data's appearance and motion semantic representation consistency to handle anomaly detection. We design a two-stream encoder to encode the appearance and motion information representations of normal samples. Lower consistency of appearance and motion features of anomalous samples can be used to generate predicted frames with larger reconstruction error.
  arXiv  Detail & Related papers  (2022-04-08T15:59:57Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• 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)
• Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features [8.407188666535506]
  Most existing methods use an autoencoder to learn to reconstruct normal videos. We propose an implicit two-path AE (ITAE), a structure in which two encoders implicitly model appearance and motion features. For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features. NF models intensify ITAE performance by learning normality through implicitly learned features.
  arXiv  Detail & Related papers  (2020-10-15T05:02:02Z)

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.