Papers › EVAL: Explainable Video Anomaly Localization
EVAL: Explainable Video Anomaly Localization
Ashish Singh, Michael J. Jones, Erik Learned-Miller
We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | CUHK Avenue | EVAL | AUC | 86.02% | #28 of 35 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | EVAL | RBDC | 68.2 | #28 of 35 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | EVAL | TBDC | 87.56 | #28 of 35 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech | EVAL | AUC | 76.63% | #22 of 31 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech | EVAL | RBDC | 59.21 | #22 of 31 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech | EVAL | TBDC | 89.44 | #22 of 31 | 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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