  Anomaly detection in surveillance videos is a challenging task due to the
diversity of anomalous video content and duration. In this paper, we consider
video anomaly detection as a regression problem with respect to anomaly scores
of video clips under weak supervision. Hence, we propose an anomaly detection
framework, called Anomaly Regression Net (AR-Net), which only requires
video-level labels in training stage. Further, to learn discriminative features
for anomaly detection, we design a dynamic multiple-instance learning loss and
a center loss for the proposed AR-Net. The former is used to enlarge the
inter-class distance between anomalous and normal instances, while the latter
is proposed to reduce the intra-class distance of normal instances.
Comprehensive experiments are performed on a challenging benchmark:
ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly
detection on ShanghaiTech dataset
