  Anomaly identification is highly dependent on the relationship between the
object and the scene, as different/same object actions in same/different scenes
may lead to various degrees of normality and anomaly. Therefore, object-scene
relation actually plays a crucial role in anomaly detection but is inadequately
explored in previous works. In this paper, we propose a Spatial-Temporal
Relation Learning (STRL) framework to tackle the video anomaly detection task.
First, considering dynamic characteristics of the objects as well as scene
areas, we construct a Spatio-Temporal Auto-Encoder (STAE) to jointly exploit
spatial and temporal evolution patterns for representation learning. For better
pattern extraction, two decoding branches are designed in the STAE module, i.e.
an appearance branch capturing spatial cues by directly predicting the next
frame, and a motion branch focusing on modeling the dynamics via optical flow
prediction. Then, to well concretize the object-scene relation, a Relation
Learning (RL) module is devised to analyze and summarize the normal relations
by introducing the Knowledge Graph Embedding methodology. Specifically in this
process, the plausibility of object-scene relation is measured by jointly
modeling object/scene features and optimizable object-scene relation maps.
Extensive experiments are conducted on three public datasets, and the superior
performance over the state-of-the-art methods demonstrates the effectiveness of
our method.
