Anomaly Detection On Ucsd Ped2
Metrics
AUC
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | AUC |
|---|---|
| context-recovery-and-knowledge-retrieval-a | 97.1% |
| a-scene-agnostic-framework-with-adversarial | 98.7% |
| spatio-temporal-predictive-tasks-for-abnormal | 98.9% |
| diversity-measurable-anomaly-detection | 99.7% |
| self-distilled-masked-auto-encoders-are | 95.4% |
| making-anomalies-more-anomalous-video-anomaly | 98.2% |
| vald-gan-video-anomaly-detection-using-latent | 97.74 |
| attention-based-residual-autoencoder-for | 97.4% |
| fastano-fast-anomaly-detection-via-spatio | 96.3% |
| stemgan-spatio-temporal-generative | 97.5 |
| follow-the-rules-reasoning-for-video-anomaly | 97.9% |
| mulde-multiscale-log-density-estimation-via | 99.7% |
| diversity-measurable-anomaly-detection | 90.2% |