Video Anomaly Detection On Shanghaitech 4
Métriques
AUC
RBDC
TBDC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUC | RBDC | TBDC | Paper Title | Repository |
|---|---|---|---|---|---|
| PGM | 61.28% | 45.4 | 81.87 | Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified | |
| VideoPatchCore | 85.1% | - | - | VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection | |
| AnomalyRuler | 85.2% | - | - | Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models | |
| AnyAnomaly | 79.7% | - | - | AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM | |
| MULDE-frame-centric-micro | 81.3% | - | - | MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection | |
| TSGAD | 80.6% | - | - | An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction | |
| MULDE-object-centric-micro | 86.7% | - | - | MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection |
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