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Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies
[article]
2021
arXiv
pre-print
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Video anomaly detection has proved to be a challenging task owing to its unsupervised training procedure and high spatio-temporal complexity existing in real-world scenarios. ...
To address this issue, we propose a novel yet efficient method named Ano-Graph for learning and modeling the interaction of normal objects. ...
STG Generation: spatio-temporal graph i.e. G st of a video with T frames is made by using the spatial G space t and the temporal G time t graphs for all timestamps t ∈ T . ...
arXiv:2103.10502v2
fatcat:g3uhxx7kgbgabdsyj5b76be6fm
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2112.04294v1 fatcat:wsh4fow3ercfxg4lej7k7mejxy
Deep learning models have been widely used for anomaly detection in surveillance videos. ...
In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different ...
We name it Hierarchical Spatial-Temporal Graph Convolutional Neural Network (HSTGCNN). ...
arXiv:2112.04294v2
fatcat:vi26nkpf2jhc7b2sjseo4b4rii
A Self-Attention Augmented Graph Convolutional Clustering Networks for Skeleton-Based Video Anomaly Behavior Detection
2021
Applied Sciences
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As far as we know, we are the first group to utilize self-attention for video anomaly detection tasks by enhancing spatial temporal graph convolution. ...
In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. ...
Conclusions In this work, we propose a novel spatial temporal self-attention augmented graph convolutional clustering networks for skeleton-based video anomaly detection tasks by employing the SAA-STGCAE ...
doi:10.3390/app12010004
fatcat:2cufica45rapfluo74b4563ezu
Graph Embedded Pose Clustering for Anomaly Detection
[article]
2020
arXiv
pre-print
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We propose a new method for anomaly detection of human actions. Our method works directly on human pose graphs that can be computed from an input video sequence. ...
The second is a coarse-grained anomaly detection data set (e.g., a Kinetics-based data set) where few actions are considered normal, and every other action should be considered abnormal. ...
We propose a deep temporal graph autoencoder based architecture for embedding the temporal pose graphs. ...
arXiv:1912.11850v2
fatcat:ok7ida67bjelnls2nftqp5ppoi
Weakly Supervised Graph Convolutional Neural Network for Human Action Localization
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
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To address this problem, we propose a skeleton-based human action recognition and localization method using weakly supervised graph convolutional neural networks, which are both spatially and temporally ...
Skeleton-based human action recognition from video sequences is currently an active topic of research. ...
Acknowledgments This work was supported by Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research Grant Numbers JP19K20310. ...
doi:10.1109/wacv45572.2020.9093551
dblp:conf/wacv/MikiCD20
fatcat:vj67gbtwnbcq5jquxrxlaaixyi
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be ...
either temporal associations or anatomical junctions. ...
Temporal convolutional networks (TCNs) are used on top of normal convolutional networks to capture temporal features. ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
Deep social force network for anomaly event detection
2021
IET Image Processing
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Anomaly event detection is vital in surveillance video analysis. However, how to learn the discriminative motion in the crowd scene is still not tackled. ...
The experiments on UCF-Crime and ShanghaiTech datasets demonstrate that our method can predict the temporal localization of anomaly events and outperform the stateof-the-art methods. ...
The aggregation model [20] , spatial and temporal constrained frame prediction [23] , temporally coherent sparse coding RNN [23] , stacked RNN auto-encoder [24] , skeleton GRU [13] , skeleton graph ...
doi:10.1049/ipr2.12299
fatcat:swdktlrbtnad5dwib35v3sq7s4
Spectral Graph Skeletons for 3D Action Recognition
[chapter]
2015
Lecture Notes in Computer Science
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We present spectral graph skeletons (SGS), a novel graphbased method for action recognition from depth cameras. ...
The contribution of this paper is to leverage a spectral graph wavelet transform (SGWT) for creating an overcomplete representation of an action signal lying on a 3D skeleton graph. ...
The first author acknowledges the Japanese Government (Monbukagakusho:MEXT) scholarship support for carrying out this research. ...
doi:10.1007/978-3-319-16817-3_27
fatcat:fnif5znejnhjboi2or77gzbezy
2021 16TH IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
2021
2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
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Masked Batch Normalization to Improve Tracking-Based Sign Language Recognition Using Graph Convolutional Networks Natsuki Takayama; Gibran Benitez-Garcia; Hiroki Takahashi 31. ...
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition Panagiotis Antoniadis; Panagiotis P Filntisis; Petros Maragos 47. ...
doi:10.1109/fg52635.2021.9667043
fatcat:q67llypbybbrbacdiyia6hs2pe
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TMM 2021 1442-1453 Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes. ...
., +, TMM 2021 1367-1382 Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Cross-view human action recognition from depth maps using spectral graph sequences
2017
Computer Vision and Image Understanding
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We evaluate two view-invariant graph types: skeleton-based and keypoint-based. ...
The skeleton-based descriptor captures the spatial pose of the subject, whereas the keypoint-based is able to capture complementary information about human-object interaction and the shape of the point ...
Acknowledgments We thank the anonymous reviewers for their insightful comments, which helped improve the content of the paper. ...
doi:10.1016/j.cviu.2016.10.004
fatcat:d55ao2jgwvaw5eroyou5r2a7ee
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
Multi Chunk Learning Based Auto Encoder for Video Anomaly Detection
2022
Intelligent Automation and Soft Computing
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The proposed method improves the accuracy of video anomaly detection by obtaining more vital information. ...
Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. ...
[22] propose a prediction network based on spatial temporal graph convolutional networks for skeleton-based video anomaly detection. ...
doi:10.32604/iasc.2022.027182
fatcat:t4mi4uqepngi5oqkgdvqsujose
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition
[article]
2022
arXiv
pre-print
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Thus, we attempt to effectively aggregate the discriminative information of actions and interactions from both RGB videos and skeleton sequences by attentively fusing multi-modal features. ...
(ESE) attentions for attentively fusing the multi-modal features in the modal and channel-wise ways. ...
For example, Yan et al. [11] proposed a Spatial-Temporal Graph Convolutional Network (ST-GAN) that employs graph convolution to aggregate the joint features in the spatial dimension. Liu et al. ...
arXiv:2112.10992v2
fatcat:ersbt5s7r5f5teurcormauwuxa
Intelligent video surveillance: a review through deep learning techniques for crowd analysis
2019
Journal of Big Data
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Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models. ...
Among them violence detection is difficult to handle since it involves group activity. ...
Spatial temporal convolutional neural networks for anomaly detection and localization in crowded scenes [114] shows the problem related with crowd analysis is challenging because of the following reasons ...
doi:10.1186/s40537-019-0212-5
fatcat:mh7d5d5c5zeczf5sdmgwz3claq
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