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Anomaly Detection In Surveillance Videos

"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)

Papers

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Showing 26–50 of 66 papers

TitleStatusHype
FOR THE SAKE OF PRIVACY: SKELETON-BASED SALIENT BEHAVIOR RECOGNITION—0
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence DetectionCode1
Anomaly detection in surveillance videos using transformer based attention modelCode1
Attention-based residual autoencoder for video anomaly detectionCode1
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Audio-Guided Attention Network for Weakly Supervised Violence DetectionCode1
VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person DetectionCode1
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos—0
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly DetectionCode1
10 Security and Privacy Problems in Large Foundation Models—0
Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions—0
HR-Crime: Human-Related Anomaly Detection in Surveillance Videos—0
FastAno: Fast Anomaly Detection via Spatio-temporal Patch TransformationCode1
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
ADNet: Temporal Anomaly Detection in Surveillance VideosCode1
MIST: Multiple Instance Self-Training Framework for Video Anomaly DetectionCode1
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures—0
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude LearningCode1
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm RateCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Localizing Anomalies from Weakly-Labeled VideosCode1
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