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Anomaly Detection In Surveillance Videos

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"The goal of a practical anomaly detection system is to timely signal an activity that deviates normal patterns and identify the time window of the occurring anomaly. [It] can be considered as coarse level video understanding, which filters out anomalies from normal patterns." A critical task in video surveillance is detecting anomalous events such as traffic accidents, crimes or illegal activities. Anomalous events rarely occur as compared to normal activities. Hence the application of this task is to "alleviate the waste of labor and time, developing intelligent computer vision algorithms for automatic video anomaly detection".

(Credit: Real-world Anomaly Detection in Surveillance Videos)

논문

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Anomaly Detection In Surveillance Videos

논문

Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping

Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection

ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications

CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos

STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications

Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method

Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection

Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network

MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos

Abnormal Event Detection In Videos Using Deep Embedding

A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos

Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection

Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection

MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection

BatchNorm-based Weakly Supervised Video Anomaly Detection

A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views

Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection

Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space

Contrastive-Regularized U-Net for Video Anomaly Detection

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MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection

Normalizing Flows for Human Pose Anomaly Detection

Self-supervised Sparse Representation for Video Anomaly Detection

Consistency-based Self-supervised Learning for Temporal Anomaly Localization

Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection

Anomaly detection in surveillance videos using transformer based attention model

A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection

Audio-Guided Attention Network for Weakly Supervised Violence Detection

A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

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Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions

HR-Crime: Human-Related Anomaly Detection in Surveillance Videos

FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation

Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video

Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning