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Object-centric and memory-guided normality reconstruction for video anomaly detection
[article]
2022
arXiv
pre-print
Preserved Fulltext
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]
2021
arXiv
pre-print
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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
2020
IEEE Access
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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
2020
Information Sciences
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2020 pre-print arXiv:2003.13528v1 fatcat:ny267pxkpfdvrmsbfhow5mouri
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]
2021
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
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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]
2020
Lecture Notes in Computer Science
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2021 pre-print arXiv:2104.01478v1 fatcat:x6mqy56zp5dmlbbluiehoutywq
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]
2021
arXiv
pre-print
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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
2021
IEEE Access
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[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]
2021
arXiv
pre-print
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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
2021
IEEE Access
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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]
2021
arXiv
pre-print
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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
2017
International Journal of Students Research in Technology & Management
Preserved Fulltext
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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
2017
International Journal of Students Research in Technology & Management
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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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