Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos

Understand

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.