计算机技术与发展2026，Vol.36Issue(4)：69-77,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0286
基于多尺度融合和双记忆单元的视频异常检测
Video Anomaly Detection Based on Multi-scale Fusion and Dual Memory Units
摘要
Abstract
In the task of weakly supervised video anomaly detection,existing methods often neglect the learning of the temporal patterns of normal behaviors due to excessive focus on the modeling of abnormal features,resulting in a blurred boundary for distinguishing normal from abnormal in the feature space,which in turn leads to problems of positioning deviation and high false alarm rate.We propose a video anomaly detection model based on multi-scale fusion and dual memory units.This model designs a multi-scale fusion module(MSTF)to extract the details of short-sequence actions and the context of long-sequence events in parallel.After adaptive convolution fusion,a feature representation containing multi-granularity temporal semantics is generated to solve the problem of context loss in traditional single-scale convolution.The dual-branch dynamic position attention mechanism(DPAM)is proposed.The time branch is adopted to model the inter-frame dependency pattern.The content branch uses sine and cosine position coding and weighted fusion of at-tention scores to accurately capture the temporal dependency between video frames and significantly improve the temporal positioning accuracy of abnormal events.The introduction of channel gating mechanisms reduces information overlap and noise interference between different channels,and strengthens abnormally correlated channels,effectively solving the problem of information redundancy during feature fusion.The experimental results show that the AP index value of the proposed method on the XD-Violence dataset reaches 82.42%,and the AUC index value on the UCF-Crime dataset reaches 86.71%,which is superior to most mainstream methods,especially the performance on the XD-Violence dataset is more outstanding.It is 2.8 percentage points higher than the baseline and has certain competitiveness.关键词
视频异常检测/多尺度融合/双记忆单元/双分支动态位置注意力/通道门控机制Key words
video anomaly detection/multi-scale fusion/dual memory unit/dual-branch dynamic position attention/channel gating mechanisms分类
信息技术与安全科学引用本文复制引用
张艳,赵月爱,孔李沛,王玲..基于多尺度融合和双记忆单元的视频异常检测[J].计算机技术与发展,2026,36(4):69-77,9.基金项目
山西省科技战略研究专项重点项目(202304031401011) （202304031401011）
太原师范学院研究生实践创新项目(SYYJSYC-2590) （SYYJSYC-2590）
