Anomaly Detection In Surveillance Videos
36 papers with code • 5 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Anomaly Detection In Surveillance Videos
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ShanghaiTech Campus
UCF-Crime
XD-Violence
UBI-Fights
VFP290K
CamNuvem Dataset
Latest papers
Anomaly detection in surveillance videos using transformer based attention model
kapildeshpande/anomaly-detection-in-surveillance-videos
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Therefore it is important to extract better quality features from the available videos.
Attention-based residual autoencoder for video anomaly detection
vt-le/astnet
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Applied Intelligence 2022
Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others.
Audio-Guided Attention Network for Weakly Supervised Violence Detection
Aaron-Pu/cma_xdVioDet
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Conference 2022
Detecting violence in video is a challenging task due to its complex scenarios and great intra-class variability.
VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection
DASH-Lab/VFP290K
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NeurIPS 2021 Track Datasets and Benchmarks 2022
Accordingly, detection of these anomalous events is of paramount importance for a number of applications, including but not limited to CCTV surveillance, security, and health care.
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
Yui010206/MoPRL
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Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise.
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
codnjsqkr/FastAno_official
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Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos.
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video
The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve the extremely uneven human supervision resource and digital cameras.
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
wanboyang/Anomaly_AR_Net_ICME_2020
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Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration.
ADNet: Temporal Anomaly Detection in Surveillance Videos
hibrahimozturk/temporal_anomaly_detection
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Additionally, we propose to use F1@k metric for temporal anomaly detection.
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
fjchange/MIST_VAD
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CVPR 2021
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations.
