This project applied computer vision and mechine learning methods aimed to detect abnormal behaved object in crowd.
1.UCSD Anomaly Detection Dataset
The UCSD Anomaly Detection Dataset was acquired with a stationary camera mounted at an elevation, overlooking pedestrian walkways. The crowd density in the walkways was variable, ranging from sparse to very crowded. In the normal setting, the video contains only pedestrians. Abnormal events are due to either:
the circulation of non pedestrian entities in the walkways
anomalous pedestrian motion patterns.
👌 Check here: http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm
2. UCF Crime
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.
👌 Check here: https://www.kaggle.com/mission-ai/crimeucfdataset
Evaluation Metric: we used frame based receiver operating characteristic (ROC) curve and corresponding area under the curve (AUC) to evaluate the performance of the model: Achieving higher results than the original paper.
| Metric | Value |
|---|---|
ACCURACY |
0.82 |
F1 SCORE |
0.33 |