文献类型：会议论文

英文题名：Efficient Video Anomaly Detection via Scene-Dependent Memory Assisted Inter-Frame RGB Difference Reconstruction

作者：Hu, Han[1];Du, Wenli[1];Wang, Bing[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：2025 Conference on Multimedia-MM

会议日期：OCT 27-31, 2025

会议地点：Dublin, IRELAND

语种：英文

外文关键词：video anomaly detection; memory network; RGB difference reconstruction

摘要：In video anomaly detection task, to mitigate the interference of background noise on learning the appearance and motion features of foreground objects, existing object-centric methods often directly disregard scene information, making it challenging for them to detect scene-dependent anomalies. Moreover, we observe that most methods focus on reconstructing or predicting complete framelevel or object-level RGB information, which limits their inference speed. In this work, we propose a novel inter-frame RGB difference reconstruction network for efficient video anomaly detection. Specifically, we construct separate scene-dependent memory banks (SDMBs) for different scenes to store exclusive normal patterns, thus enabling sensitive detection of scene-dependent anomalies. Meanwhile, we design sparse aggregation and similarity-driven updating mechanisms for the memory items in the SDMBs, which effectively increase the reconstruction error of anomalies by adequately learning the diverse normal patterns in both the training and testing data, thus making the distinction between normal and abnormal frames easier. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches in terms of detection accuracy, false negative rate, and inference speed, particularly in situations with more complex scene and event types.