  Detection of fights is an important surveillance application in videos. Most
existing methods use supervised binary action recognition. Since frame-level
annotations are very hard to get for anomaly detection, weakly supervised
learning using multiple instance learning is widely used. This paper explores
the detection of fights in videos as one special type of anomaly detection and
as binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets
for most of the study since they have frame-level annotations. We find that the
anomaly detection has similar or even better performance than the action
recognition. Furthermore, we study to use anomaly detection as a toolbox to
generate training datasets for action recognition in an iterative way
conditioned on the performance of the anomaly detection. Experiment results
should show that we achieve state-of-the-art performance on three fight
detection datasets.
