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Future frame prediction based on generative assistant discriminative network for anomaly detection

https://doi.org/10.1007/s10489-022-03488-2

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Trajectory outlier detection approach based on common slices sub-sequence

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Surveillance video anomaly detection via non-local U-Net frame prediction

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DDR-ID: dual deep reconstruction networks based image decomposition for anomaly detection

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Region-based scalable smart system for anomaly detection in pedestrian walkways

inferredcites →https://doi.org/10.1016/j.cviu.2018.02.006openalex-snapshot

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

inferredcites →https://doi.org/10.1016/j.cviu.2020.102920openalex-snapshot

Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder

inferredcites →https://doi.org/10.1016/j.imavis.2020.103915openalex-snapshot

Anomaly detection in surveillance video based on bidirectional prediction

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A review on classifying abnormal behavior in crowd scene

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Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

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An adaptive training-less framework for anomaly detection in crowd scenes

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Local distinguishability aggrandizing network for human anomaly detection

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ARAE: Adversarially robust training of autoencoders improves novelty detection

inferredcites →https://doi.org/10.1016/j.patcog.2021.107969openalex-snapshot

NM-GAN: Noise-modulated generative adversarial network for video anomaly detection

inferredcites →https://doi.org/10.1016/j.patrec.2019.11.024openalex-snapshot

Integrating prediction and reconstruction for anomaly detection

inferredcites →https://doi.org/10.1049/iet-ipr.2016.0044openalex-snapshot

Anomaly detection and localisation in the crowd scenes using a block‐based social force model

inferredcites →https://doi.org/10.1049/iet-ipr.2019.0769openalex-snapshot

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

inferredcites →https://doi.org/10.1109/avss.2019.8909850openalex-snapshot

Future Frame Prediction Using Convolutional VRNN for Anomaly Detection

inferredcites →https://doi.org/10.1109/cvpr.2018.00684openalex-snapshot

Future Frame Prediction for Anomaly Detection - A New Baseline

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01438openalex-snapshot

Learning Memory-Guided Normality for Anomaly Detection

inferredcites →https://doi.org/10.1109/iccv.2017.315openalex-snapshot

Unmasking the Abnormal Events in Video

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Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

inferredcites →https://doi.org/10.1109/tcds.2018.2883368openalex-snapshot

Abnormal Event Detection From Videos Using a Two-Stream Recurrent Variational Autoencoder

inferredcites →https://doi.org/10.1109/tcsvt.2016.2637778openalex-snapshot

Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos

inferredcites →https://doi.org/10.1109/tcsvt.2019.2929855openalex-snapshot

Efficient Privacy-Preserving Anomaly Detection and Localization in Bitstream Video

inferredcites →https://doi.org/10.1109/tcsvt.2019.2962229openalex-snapshot

Attention-Driven Loss for Anomaly Detection in Video Surveillance

inferredcites →https://doi.org/10.1109/tcsvt.2020.2984783openalex-snapshot

Video Frame Prediction by Deep Multi-Branch Mask Network

inferredcites →https://doi.org/10.1109/tevc.2019.2895748openalex-snapshot

Evolutionary Generative Adversarial Networks

inferredcites →https://doi.org/10.1109/tgrs.2020.2965961openalex-snapshot

Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

inferredcites →https://doi.org/10.1109/tgrs.2020.2982406openalex-snapshot

Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning

inferredcites →https://doi.org/10.1109/tifs.2019.2900907openalex-snapshot

AnomalyNet: An Anomaly Detection Network for Video Surveillance

inferredcites →https://doi.org/10.1109/tifs.2020.3023791openalex-snapshot

NeuroAED: Towards Efficient Abnormal Event Detection in Visual Surveillance With Neuromorphic Vision Sensor

inferredcites →https://doi.org/10.1109/tip.2017.2670780openalex-snapshot

Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

inferredcites →https://doi.org/10.1109/tip.2019.2948286openalex-snapshot

BMAN: Bidirectional Multi-Scale Aggregation Networks for Abnormal Event Detection

inferredcites →https://doi.org/10.1109/tmi.2021.3073986openalex-snapshot

GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs

inferredcites →https://doi.org/10.1109/tmm.2018.2846411openalex-snapshot

Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos

inferredcites →https://doi.org/10.1109/tmm.2019.2929931openalex-snapshot

Video Anomaly Detection and Localization Based on an Adaptive Intra-Frame Classification Network

inferredcites →https://doi.org/10.1109/tmm.2019.2950530openalex-snapshot

Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos

inferredcites →https://doi.org/10.1109/tmm.2020.2984093openalex-snapshot

Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes

inferredcites →https://doi.org/10.1109/tnnls.2020.2979049openalex-snapshot

Deep End-to-End One-Class Classifier

inferredcites →https://doi.org/10.1109/tpami.2019.2944377openalex-snapshot

Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

inferredcites →https://doi.org/10.1109/tpami.2021.3129349openalex-snapshot

Future Frame Prediction Network for Video Anomaly Detection

inferredcites →https://doi.org/10.1109/wacv.2019.00206openalex-snapshot

Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds

inferredcites →https://doi.org/10.1145/3439950openalex-snapshot

Deep Learning for Anomaly Detection

inferredcites →https://doi.org/10.3233/jifs-169908openalex-snapshot

Transfer learning for video anomaly detection

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Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset

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Urban Video Surveillance as a Tool to Improve Security in Public Spaces

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Cross-Modality Integration Framework for Anomaly Detection in Surveillance Videos

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Transformer with Spatio-Temporal Representation for Video Anomaly Detection

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An intelligent trusted edge data production method for distributed Internet of things

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Anomaly detection in surveillance videos using Transformer with margin learning

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STemGAN: spatio-temporal generative adversarial network for video anomaly detection

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Multi-scale spatiotemporal normality learning for unsupervised video anomaly detection

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MMI-FM: Multimodal Interaction and Fusion Mechanism for Video Anomaly Detection

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FOAD: a novel video anomaly detection focusing on objects

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MTM-net: a multidimensional two-stage memory-guided network for vedio abnormal detection

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Grey-adversary perceptual network for anomaly detection

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A novel spatio-temporal memory network for video anomaly detection

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A novel reconstruction-based video anomaly detection with idempotent generative network

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Learning video normality for anomaly detection via multi-scale spatiotemporal feature extraction and a feature memory module

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Spatial scene temporal behavior framework for anomaly detection

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Attention-guided generator with dual discriminator GAN for real-time video anomaly detection

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Deep learning based detection of cyber-attacks on image sequence in industrial control

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Memory-guided diffusion framework for pose-based Video Anomaly Detection

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Multi-level feature splicing 3D network based on multi-task joint learning for video anomaly detection

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Cross-modality integration framework with prediction, perception and discrimination for video anomaly detection

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Video Analysis and Frame Prediction Based on Improved Object Detection and ConvGRU

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OSAD: Open-Set Supervised Anomaly Detection in Surveillance Videos Based on Margin Metric Learning

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Multi-Perceptual Framework for Anomaly Detection

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Crowd Behaviour Prediction using Visual and Location Data in Super-Crowded Scenarios

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Spatiotemporal Masked Autoencoder with Multi-Memory and Skip Connections for Video Anomaly Detection

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Online Video Anomaly Detection

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YOLO-ABD: A Multi-Scale Detection Model for Pedestrian Anomaly Behavior Detection

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Text Classification Using Deep Learning Models: A Comparative Review

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