  Abnormal event detection in video is a challenging vision problem. Most
existing approaches formulate abnormal event detection as an outlier detection
task, due to the scarcity of anomalous data during training. Because of the
lack of prior information regarding abnormal events, these methods are not
fully-equipped to differentiate between normal and abnormal events. In this
work, we formalize abnormal event detection as a one-versus-rest binary
classification problem. Our contribution is two-fold. First, we introduce an
unsupervised feature learning framework based on object-centric convolutional
auto-encoders to encode both motion and appearance information. Second, we
propose a supervised classification approach based on clustering the training
samples into normality clusters. A one-versus-rest abnormal event classifier is
then employed to separate each normality cluster from the rest. For the purpose
of training the classifier, the other clusters act as dummy anomalies. During
inference, an object is labeled as abnormal if the highest classification score
assigned by the one-versus-rest classifiers is negative. Comprehensive
experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and
UMN. Our approach provides superior results on all four data sets. On the
large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4%
in terms of frame-level AUC compared to the state-of-the-art method [Sultani et
al., CVPR 2018].
