Anomaly Detection In Surveillance Videos On
Métriques
Decidability
EER
ROC AUC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Decidability | EER | ROC AUC |
|---|---|---|---|
| weakly-and-partially-supervised-learning | 0.885 | 0.302 | 75.90 |
| contrastive-regularized-u-net-for-video | - | - | 85.24 |
| stead-spatio-temporal-efficient-anomaly-1 | - | - | 88.87 |
| mulde-multiscale-log-density-estimation-via | - | - | 78.5% |
| stead-spatio-temporal-efficient-anomaly-1 | - | - | 91.34 |
| a-multi-stream-deep-neural-network-with-late | - | - | 84.48 |
| mgfn-magnitude-contrastive-glance-and-focus | - | - | 86.98 |
| mist-multiple-instance-self-training | - | - | 82.30 |
| real-world-anomaly-detection-in-surveillance | 0.613 | 0.353 | 75.41 |
| 3d-resnet-with-ranking-loss-function-for | - | - | 76.67 |
| learning-prompt-enhanced-context-features-for | - | - | 86.76 |
| weakly-supervised-video-anomaly-detection | - | - | 84.03 |
| self-supervised-sparse-representation-for | - | - | 85.99 |
| multiple-instance-based-video-anomaly | - | - | 80.10 |
| batchnorm-based-weakly-supervised-video | - | - | 87.24 |
| anomalous-event-recognition-in-videos-based | - | - | 81.91 |
| localizing-anomalies-from-weakly-labeled | - | - | 85.38 |