  Formulating learning systems for the detection of real-world anomalous events
using only video-level labels is a challenging task mainly due to the presence
of noisy labels as well as the rare occurrence of anomalous events in the
training data. We propose a weakly supervised anomaly detection system which
has multiple contributions including a random batch selection mechanism to
reduce inter-batch correlation and a normalcy suppression block which learns to
minimize anomaly scores over normal regions of a video by utilizing the overall
information available in a training batch. In addition, a clustering loss block
is proposed to mitigate the label noise and to improve the representation
learning for the anomalous and normal regions. This block encourages the
backbone network to produce two distinct feature clusters representing normal
and anomalous events. Extensive analysis of the proposed approach is provided
using three popular anomaly detection datasets including UCF-Crime,
ShanghaiTech, and UCSD Ped2. The experiments demonstrate a superior anomaly
detection capability of our approach.
