Papers › Abnormal Event Detection in Videos using Generative Adversarial Nets
Abnormal Event Detection in Videos using Generative Adversarial Nets
Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, Nicu Sebe
In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Abnormal Event Detection In Video | UBI-Fights | Adversarial Generator | AUC | 0.533 | #5 of 6 | Archive leaderboard | report |
| Abnormal Event Detection In Video | UBI-Fights | Adversarial Generator | Decidability | 0.147 | #5 of 6 | Archive leaderboard | report |
| Abnormal Event Detection In Video | UBI-Fights | Adversarial Generator | EER | 0.484 | #5 of 6 | Archive leaderboard | report |
| Abnormal Event Detection In Video | UCSD Ped2 | Adversarial Generator | AUC | 97.4% | #4 of 4 | Archive leaderboard | report |
| Semi-supervised Anomaly Detection | UBI-Fights | Adversarial Generator | AUC | 0.533 | #6 of 7 | Archive leaderboard | report |
| Semi-supervised Anomaly Detection | UBI-Fights | Adversarial Generator | Decidability | 0.147 | #6 of 7 | Archive leaderboard | report |
| Semi-supervised Anomaly Detection | UBI-Fights | Adversarial Generator | EER | 0.484 | #6 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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