Abnormal Event Detection In Video On Ubi
Metrics
AUC
Decidability
EER
Results
Performance results of various models on this benchmark
| Paper Title | ||||
|---|---|---|---|---|
| GMM | 0.906 | 1.386 | 0.160 | Weakly and Partially Supervised Learning Frameworks for Anomaly Detection |
| Sultani et al. | 0.892 | 0.804 | 0.186 | Real-world Anomaly Detection in Surveillance Videos |
| s2-VAE | 0.610 | 0.323 | 0.427 | Generative Neural Networks for Anomaly Detection in Crowded Scenes |
| LSTM-VAE | 0.541 | 0.059 | 0.480 | Abnormal Event Detection in Videos using Spatiotemporal Autoencoder |
| Adversarial Generator | 0.533 | 0.147 | 0.484 | Abnormal Event Detection in Videos using Generative Adversarial Nets |
| Hasan et al. | 0.528 | 0.194 | 0.466 | Learning Temporal Regularity in Video Sequences |
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