  In contemporary society, surveillance anomaly detection, i.e., spotting
anomalous events such as crimes or accidents in surveillance videos, is a
critical task. As anomalies occur rarely, most training data consists of
unlabeled videos without anomalous events, which makes the task challenging.
Most existing methods use an autoencoder (AE) to learn to reconstruct normal
videos; they then detect anomalies based on their failure to reconstruct the
appearance of abnormal scenes. However, because anomalies are distinguished by
appearance as well as motion, many previous approaches have explicitly
separated appearance and motion information-for example, using a pre-trained
optical flow model. This explicit separation restricts reciprocal
representation capabilities between two types of information. In contrast, we
propose an implicit two-path AE (ITAE), a structure in which two encoders
implicitly model appearance and motion features, along with a single decoder
that combines them to learn normal video patterns. For the complex distribution
of normal scenes, we suggest normal density estimation of ITAE features through
normalizing flow (NF)-based generative models to learn the tractable
likelihoods and identify anomalies using out of distribution detection. NF
models intensify ITAE performance by learning normality through implicitly
learned features. Finally, we demonstrate the effectiveness of ITAE and its
feature distribution modeling on six benchmarks, including databases that
contain various anomalies in real-world scenarios.

Computer Vision and Pattern Recognition