  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.
Our experiments show that a network trained with spatiotemporal tubes performs
better than its analogous model trained with whole-frame videos. In addition,
we discover that the locality is robust to different kinds of errors in the
tube extraction phase at test time. Finally, we demonstrate that our network
can provide spatiotemporal proposals for unseen surveillance videos leveraging
only video-level labels. By doing, we enlarge our spatiotemporal anomaly
dataset without the need for further human labeling.
