The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety.
This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities.
Source: Video Anomaly Dection Dataset
Variants: UCF-Crime
This dataset is used in 2 benchmarks:
| Task | Model | Paper | Date |
|---|---|---|---|
| Anomaly Detection | MULDE-frame-centric-micro-one-class-classification | MULDE: Multiscale Log-Density Estimation via … | 2024-03-21 |
| Video Anomaly Detection | MULDE-frame-centric-micro-one-class-classification | MULDE: Multiscale Log-Density Estimation via … | 2024-03-21 |
Recent papers with results on this dataset: