  Surveillance video anomaly detection is to detect events that rarely or never
happened in a certain scene. The generation error (GE)-based methods exhibit
excellent performance on this task. They firstly train a generative neural
network (GNN) to generate normal samples, then judge the samples with large GEs
as anomalies. Almost all the GE-based methods utilize frame-level GEs to detect
anomalies. However, anomalies generally occur in local areas, the frame-level
GE introduces GEs of normal areas to anomaly discriminations, that brings two
problems: i) The GE of normal areas reduces the anomaly saliency of the
anomalous frame. ii) Different videos have different normal-GE-levels, thus it
is hard to set a uniform threshold for all videos to detect anomalies. To
address these problems, we propose a promotion method: utilize the maximum of
block-level GEs on the frame to detect anomaly. Firstly, we calculate the
block-level GEs at each position on the frame. Then, we utilize the maximum of
the block-level GEs on the frame to detect anomalies. Based on the existed GNN
models, experiments are carried out on multiple datasets. The results
demonstrate the effectiveness of the proposed method and achieve
state-of-the-art performance.
