Anomaly Locality in Video Surveillance

Understand

This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos.

• Although anomalies are generally local, as they happen in a limited portion of the frame, none of the previous works on the subject has ever studied the contribution of locality.
• In this work, we explore the impact of considering spatiotemporal tubes instead of whole-frame video segments.
• For this purpose, we enrich existing surveillance videos with spatial and temporal annotations: it is the first dataset for anomaly detection with bounding box supervision in both its train and test set.