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Variational Abnormal Behavior Detection With Motion Consistency

https://doi.org/10.1109/tip.2021.3130545

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What this work says

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

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution

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Abnormal event detection via the analysis of multi-frame optical flow information

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Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder

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Movies tags extraction using deep learning

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Abnormal crowd behavior detection using novel optical flow-based features

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Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes

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Learning Temporal Regularity in Video Sequences

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Future Frame Prediction for Anomaly Detection - A New Baseline

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Visual-Based Human Crowds Behavior Analysis Based on Graph Modeling and Matching

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Histograms of optical flow orientation for abnormal events detection

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A Bayesian Model for Crowd Escape Behavior Detection

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Guest Editorial Introduction to the Special Issue on Group and Crowd Behavior Analysis for Intelligent Multicamera Video Surveillance

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Image quality assessment: from error visibility to structural similarity

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

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Multilevel Anomaly Detection Through Variational Autoencoders and Bayesian Models for Self-Aware Embodied Agents

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Cyclical Learning Rates for Training Neural Networks

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Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection

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Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection

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Hybrid Histogram of Oriented Optical Flow for Abnormal Behavior Detection in Crowd Scenes

inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot

Anomaly detection

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Crowd Abnormal Behaviour Identification Based on Integral Optical Flow in Video Surveillance Systems

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A Discrete CVAE for Response Generation on Short-Text Conversation

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OPTICAL FLOW BASED LEARNING APPROACH FOR ABNORMAL CROWD ACTIVITY DETECTION WITH MOTION DESCRIPTOR MAP

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An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders

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Auto-Encoding Variational Bayes

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Generating Videos with Scene Dynamics

What points at this work

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

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Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions

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Deep learning applied for abnormal human behavior recognition in video surveillance systems: A systematic review

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DSLSTM: a deep convolutional encoder–decoder architecture for abnormality detection in video surveillance

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QLGWYB: design of an efficient model for analyzing crowd behavior through Quad LSTM and Quad GRU fusion enhanced by Q-learning and YOLO

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K-fold matching model for crowd behavioral anomaly detection from discontinuous inputs

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CroSA: Unsupervised domain adaptation abnormal behavior detection via cross-space alignment

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MG-KG: Unsupervised video anomaly detection based on motion guidance and knowledge graph

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The use of convolutional neural networks for abnormal behavior recognition in crowd scenes

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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks

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NOMABER: A Novel Framework for Multi-Type Abnormal Behaviour Recognition

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A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

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Deep Learning-Based Abnormal Behavior Detection for Elderly Healthcare Using Consumer Network Cameras

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