A promotion method for generation error-based video anomaly detection

•An improved method is proposed for generation-error based video anomaly detection.•We utilize the maximum of the block-level generation-errors to detect anomalies.•We reduce the disturbance of generation errors of normal area to anomaly detection.•We analyze the impact of different normalization algorithms on anomaly detection.•This method achieves state-of-the-art performance on multiple datasets. 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 performances 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 detection, that brings two problems: i) The GEs of normal areas reduce 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 different 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 anomalies. 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 existing GNN models, we carry out experiments on multiple datasets. The results demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance.