Spatio-temporal graph-based CNNs for anomaly detection in weakly-labeled videos

Highlights：

• Spatial similarity graph and temporal consistency graph are constructed, and the attention mechanism is introduced to allocate attention for each segment.

• A novel spatial-temporal fusion graph module is proposed to capture the corresponding identifying information synchronously, and long-range spatial-temporal dependencies could also be extracted with layers stacked.

• We formulate a ranking loss which encourages the STGCNs to pay attention to the context around the anomalous part, and a classification loss to adapt the variation of raw videos and less abnormal events.

• We evaluate the proposed method on several anomaly detection benchmarks and it achieves excellent performance as compared to the state-of-the-art anomaly detection works.