  For weakly supervised anomaly detection, most existing work is limited to the
problem of inadequate video representation due to the inability of modeling
long-term contextual information. To solve this, we propose a novel weakly
supervised adaptive graph convolutional network (WAGCN) to model the complex
contextual relationship among video segments. By which, we fully consider the
influence of other video segments on the current one when generating the
anomaly probability score for each segment. Firstly, we combine the temporal
consistency as well as feature similarity of video segments to construct a
global graph, which makes full use of the association information among
spatial-temporal features of anomalous events in videos. Secondly, we propose a
graph learning layer in order to break the limitation of setting topology
manually, which can extract graph adjacency matrix based on data adaptively and
effectively. Extensive experiments on two public datasets (i.e., UCF-Crime
dataset and ShanghaiTech dataset) demonstrate the effectiveness of our approach
which achieves state-of-the-art performance.
