  Learning to detect real-world anomalous events through video-level labels is
a challenging task due to the rare occurrence of anomalies as well as noise in
the labels. In this work, we propose a weakly supervised anomaly detection
method which has manifold contributions including1) a random batch based
training procedure to reduce inter-batch correlation, 2) a normalcy suppression
mechanism to minimize anomaly scores of the normal regions of a video by taking
into account the overall information available in one training batch, and 3) a
clustering distance based loss to contribute towards mitigating the label noise
and to produce better anomaly representations by encouraging our model to
generate distinct normal and anomalous clusters. The proposed method
obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and
ShanghaiTech datasets respectively, demonstrating its superiority over the
existing state-of-the-art algorithms.
