Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow

• URL: http://arxiv.org/abs/2105.04302v1
• Date: Mon, 10 May 2021 12:25:00 GMT
• Title: Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow
• Authors: Hongyong Wang, Xinjian Zhang, Su Yang, Weishan Zhang
• Abstract summary: We propose to discriminate anomalies from normal ones by the duality of normality-granted optical flow. We extend the appearance-motion correspondence scheme from frame reconstruction to prediction.
• Score: 1.8065361710947974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection is a challenging task because of diverse abnormal events. To this task, methods based on reconstruction and prediction are wildly used in recent works, which are built on the assumption that learning on normal data, anomalies cannot be reconstructed or predicated as good as normal patterns, namely the anomaly result with more errors. In this paper, we propose to discriminate anomalies from normal ones by the duality of normality-granted optical flow, which is conducive to predict normal frames but adverse to abnormal frames. The normality-granted optical flow is predicted from a single frame, to keep the motion knowledge focused on normal patterns. Meanwhile, We extend the appearance-motion correspondence scheme from frame reconstruction to prediction, which not only helps to learn the knowledge about object appearances and correlated motion, but also meets the fact that motion is the transformation between appearances. We also introduce a margin loss to enhance the learning of frame prediction. Experiments on standard benchmark datasets demonstrate the impressive performance of our approach.

Related papers

• 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)
• Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency [2.395616571632115]
  We propose a framework of Appearance-Motion Semantics Consistency Representation. The two-stream structure is designed to encode the appearance and motion information representation of normal samples. A novel consistency loss is proposed to enhance the consistency of feature semantics so that anomalies with low consistency can be identified.
  arXiv  Detail & Related papers  (2023-03-09T08:28:34Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
  We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
  arXiv  Detail & Related papers  (2022-10-27T19:45:12Z)
• 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)
• FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation [6.112591965159383]
  We propose spatial rotation transformation (SRT) and temporal mixing transformation (TMT) to generate irregular patch cuboids within normal frame cuboids. Our model is evaluated on three anomaly detection benchmarks, achieving competitive accuracy and surpassing all the previous works in terms of speed.
  arXiv  Detail & Related papers  (2021-06-16T08:14:31Z)
• 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)
• Learning Memory-guided Normality for Anomaly Detection [33.77435699029528]
  We present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly. We also present novel feature compactness and separateness losses to train the memory, boosting the discriminative power of both memory items and deeply learned features from normal data.
  arXiv  Detail & Related papers  (2020-03-30T05:30:09Z)

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.