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Anomaly Detection On Ubnormal

Metrics

AUC
RBDC
TBDC

Results

Performance results of various models on this benchmark

Paper Title
STG-NF - Supervised79.2%--Normalizing Flows for Human Pose Anomaly Detection
MULDE-frame-centric-micro-one-class-classification72.8%--MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
AnomalyRuler71.9%--Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
STG-NF - Unsupervised71.8%--Normalizing Flows for Human Pose Anomaly Detection
TimeSformer68.5%0.040.05Is Space-Time Attention All You Need for Video Understanding?
MoCoDAD68.3%--Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
COSKAD-hyperbolic65%--Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
COSKAD-euclidean64.9%--Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
COSKAD-radial62.9%--Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
FPDM62.7--Feature Prediction Diffusion Model for Video Anomaly Detection
SSMTL++v162.1%25.6363.53SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
Background-Agnostic Framework61.3%25.4356.27A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
BiPOCO50.7--BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
MIL50.3%0.0020.001Real-world Anomaly Detection in Surveillance Videos
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