MPE: Multi-frame prediction error-based video anomaly detection framework for robust anomaly inference

Abstract

As video surveillance has become increasingly widespread, the necessity of video anomaly detection to support surveillance-related tasks has grown significantly. We propose a novel multi-frame prediction error-based framework (MPE) to enhance anomaly detection accuracy and efficiency. MPE mitigates false positives in prediction models by leveraging multi-frame prediction errors and reduces the time required for their generation through a frame prediction error storage method. The core idea of MPE is to reduce the prediction error of a normal frame while increasing the prediction error of an abnormal frame by leveraging the prediction errors of adjacent frames. We evaluated our method on the Ped2, Avenue, and ShanghaiTech datasets. The experimental results demonstrate that MPE improved the frame-level area under the curve (AUC) of prediction models while maintaining low computational overhead across all datasets. These results show that MPE makes prediction models robust and efficient for video anomaly detection in real-world scenarios.