  Video anomaly detection (VAD) is a vital task with great practical
applications in industrial surveillance, security system, and traffic control.
Unlike previous unsupervised VAD methods that adopt a fixed structure to learn
normality without considering different detection demands, we design a
spatial-temporal hierarchical architecture (STHA) as a configurable
architecture to flexibly detect different degrees of anomaly. The comprehensive
structure of the STHA is delineated into a tripartite hierarchy, encompassing
the following tiers: the stream level, the stack level, and the block level.
Specifically, we design several auto-encoder-based blocks that possess varying
capacities for extracting normal patterns. Then, we stack blocks according to
the complexity degrees with both intra-stack and inter-stack residual links to
learn hierarchical normality gradually. Considering the multisource knowledge
of videos, we also model the spatial normality of video frames and temporal
normality of RGB difference by designing two parallel streams consisting of
stacks. Thus, STHA can provide various representation learning abilities by
expanding or contracting hierarchically to detect anomalies of different
degrees. Since the anomaly set is complicated and unbounded, our STHA can
adjust its detection ability to adapt to the human detection demands and the
complexity degree of anomaly that happened in the history of a scene. We
conduct experiments on three benchmarks and perform extensive analysis, and the
results demonstrate that our method performs comparablely to the
state-of-the-art methods. In addition, we design a toy dataset to prove that
our model can better balance the learning ability to adapt to different
detection demands.
