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Object-centric and memory-guided normality reconstruction for video anomaly detection [article]

Khalil Bergaoui, Yassine Naji, Aleksandr Setkov, Angélique Loesch, Michèle Gouiffès, Romaric Audigier
2022 arXiv   pre-print
This paper addresses video anomaly detection problem for videosurveillance.  ...  Our framework leverages both appearance and motion information to learn object-level behavior and captures prototypical patterns within a memory module.  ...  INTRODUCTION Video Anomaly Detection (VAD) is an open research problem which consists in detecting rare occurrences of abnormal events. This is a challenging problem due to two main reasons.  ... 
arXiv:2203.03677v1 fatcat:p5qu75up6vcetjjn2xemns744y

Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings [article]

Yuandu Lai, Yahong Han, Yaowei Wang
2021 arXiv   pre-print
To address this problem, we present a novel approach for anomaly detection, which utilizes discriminative prototypes of normal data to reconstruct video frames.  ...  Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to describe normal event patterns with small reconstruction errors.  ...  CONCLUSION We present a memory-augmented two-branch autoencoder for video anomaly detection.  ... 
arXiv:2104.14945v3 fatcat:5it4644lpzbjfeapqksq5325w4

Dual Discriminator Generative Adversarial Network for Video Anomaly Detection

Fei Dong, Yu Zhang, Xiushan Nie
2020 IEEE Access  
Because of the rarity of abnormal events and the complicated characteristic of videos, video anomaly detection is challenging and has been studied for a long time.  ...  Video anomaly detection is an essential task because of its numerous applications in various areas.  ...  For video anomaly detection, spatial and temporal information are both important. To predict future frames better, we consider both appearance and motion constraints.  ... 
doi:10.1109/access.2020.2993373 fatcat:y7qrrnapcnbp3agngpwkr5csp4

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

Habtamu Fanta, Zhiwen Shao, Lizhuang Ma
2020 Information Sciences  
Empirical results show that our proposed optimized GRU model outperforms standard GRU and Long Short Term Memory (LSTM) networks on most metrics for detection and generalization tasks on CUHK Avenue and  ...  In this paper, we propose a novel version of Gated Recurrent Unit (GRU), called Single Tunnelled GRU for abnormality detection.  ...  This layer is capable of representing temporal motion patterns in video frames for anomaly detection task.  ... 
doi:10.1016/j.ins.2020.03.034 fatcat:umdrfn2llbfl3bumnp763kefcq

Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features [article]

MyeongAh Cho, Taeoh Kim, Ig-Jae Kim, Sangyoun Lee
2021 arXiv   pre-print
However, because anomalies are distinguished by appearance or motion, many previous approaches have explicitly separated appearance and motion information--for example, using a pre-trained optical flow  ...  Most existing methods use an autoencoder (AE) to learn reconstructing normal videos and detect anomalies by a failure to reconstruct the appearance of abnormal scenes.  ...  It is essential to extract features that contain the appearance and motion of the input video for anomaly detection.  ... 
arXiv:2010.07524v2 fatcat:r7tvz7lcrbg2ljugxwgtnharce

Future Frame Prediction for Anomaly Detection -- A New Baseline [article]

Wen Liu, Weixin Luo, Dongze Lian, Shenghua Gao
2018 arXiv   pre-print
Anomaly detection in videos refers to the identification of events that do not conform to expected behavior.  ...  In this paper, we propose to tackle the anomaly detection problem within a video prediction framework.  ...  Thus the appearance and motion losses based video prediction are more consistent with anomaly detection.  ... 
arXiv:1712.09867v3 fatcat:lsfq2ts22zcejc5qprpdoeitmu

Future Frame Prediction for Anomaly Detection - A New Baseline

Wen Liu, Weixin Luo, Dongze Lian, Shenghua Gao
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Anomaly detection in videos refers to the identification of events that do not conform to expected behavior.  ...  In this paper, we propose to tackle the anomaly detection problem within a video prediction framework.  ...  Thus the appearance and motion losses based video prediction are more consistent with anomaly detection.  ... 
doi:10.1109/cvpr.2018.00684 dblp:conf/cvpr/LiuLLG18 fatcat:abgv6cehnned7imw2soaykgd3a

"Forget" the Forget Gate: Estimating Anomalies in Videos Using Self-contained Long Short-Term Memory Networks [chapter]

Habtamu Fanta, Zhiwen Shao, Lizhuang Ma
2020 Lecture Notes in Computer Science  
In this paper, we present an approach of detecting anomalies in videos by learning a novel LSTM based self-contained network on normal dense optical flow.  ...  Abnormal event detection is a challenging task that requires effectively handling intricate features of appearance and motion.  ...  For abnormal event detection problems that focus on analyzing appearance and motion patterns, an LSTM-based model should keep information from previous memory for longer duration to effectively compute  ... 
doi:10.1007/978-3-030-61864-3_15 fatcat:yf4nmh4tfnb7ln6m7fgsoyrpju

A Modular and Unified Framework for Detecting and Localizing Video Anomalies [article]

Keval Doshi, Yasin Yilmaz
2021 arXiv   pre-print
Anomaly detection in videos has been attracting an increasing amount of attention.  ...  Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly  ...  Global Motion: Apart from spatial and appearance features, capturing the motion of different objects is also critical for detecting anomalies in videos.  ... 
arXiv:2103.11299v1 fatcat:cqip5pyg5bfzfgiocrmrr252p4

Bidirectional Retrospective Generation Adversarial Network for Anomaly Detection in Videos

Zhiwei Yang, Jing Liu, Peng Wu
2021 IEEE Access  
[64] model the consistency between normal video appearances and motion to tackle the video anomaly detection by an appearance-motion memory consistent network. Georgescu et al.  ...  Furthermore, in the task of video anomaly detection based on the future frame prediction, the motion consistency of objects in the predicted frame is an important factor for modeling the motion pattern  ... 
doi:10.1109/access.2021.3100678 fatcat:q6tizkldrvfmxjfx5nazxczui4

FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation [article]

Chaewon Park, MyeongAh Cho, Minhyeok Lee, Sangyoun Lee
2021 arXiv   pre-print
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos.  ...  However, a lot of prediction networks are computationally expensive owing to the use of pre-trained optical flow networks, or fail to detect abnormal situations because of their strong generative ability  ...  Conclusion and Future Work In this paper, we proposed a prediction network for video anomaly detection combined with a patch anomaly generation phase.  ... 
arXiv:2106.08613v4 fatcat:pnamkhlbe5catc5aysmcvoqjrm

An Approach to Detect Anomaly in Video using Deep Generative Network

Savathm Saypadith, Takao Onoye
2021 IEEE Access  
We proposed a "multi-scale U-Net" network architecture, the unsupervised learning for anomaly detection in video based on generative adversarial network (GAN) structure.  ...  In this paper, we present a framework to detect anomalies in video.  ...  The ShanghaiTech dataset covers challenging scenarios for video anomaly due to large variations in appearance and viewpoint, which consists of 13 scenes of 330 training and 107 testing videos.  ... 
doi:10.1109/access.2021.3126335 fatcat:g3xhkgbm6bhcnofrtz5ksk2nuy

Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection [article]

Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
2021 arXiv   pre-print
Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem.  ...  However, several studies show that, even with normal data only training, AEs can often start reconstructing anomalies as well which depletes their anomaly detection performance.  ...  This is by far the largest one-class anomaly detection dataset [21] . It consists of 330 training and 107 test videos.  ... 
arXiv:2110.09768v1 fatcat:33lrfvqgojdifnctwdidiejqca

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

Meenal Suryakant Vatsaraj, Rajan Vishnu Parab, D S Bade
2017 International Journal of Students Research in Technology & Management  
Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus  ...  Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes.  ...  ANOMALY DETECTION USING ARTIFICIAL NEURAL NETWORK Artificial neural network is the best example of machine learning which consist simple mathematical or neural network.  ... 
doi:10.18510/ijsrtm.2017.517(1) fatcat:fpfttwsj3vg5dldfbsnxhwj2me

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

Meenal Suryakant Vatsaraj, Rajan Vishnu Parab, Prof.D.S Bade
2017 International Journal of Students Research in Technology & Management  
Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus  ...  Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes.  ...  ANOMALY DETECTION USING ARTIFICIAL NEURAL NETWORK Artificial neural network is the best example of machine learning which consist simple mathematical or neural network.  ... 
doi:10.18510/ijsrtm.2017.517 fatcat:agzdwi4gp5anfnfn55bzvpehee
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