广东工业大学学报2025，Vol.42Issue(3)：130-136,7.DOI:10.12052/gdutxb.230202
基于掩蔽卷积及外部注意力的视频异常行为检测算法
Video Frame Anomaly Behavior Detection Method Based on Masked Convolution and External Attention
摘要
Abstract
In order to solve the challenges of video anomaly behavior detection,a new detection algorithm model is proposed based on masked convolution and external attention mechanism convolutional neural network.On the one hand,by masked convolution,it can restrict the effective regions of convolution to make the neural network efficiently learn,so as to model the positive moderate that is effective in the feathers of normal behavior.On the other hand,by combining with lightweight external attention mechanisms,the modeling quality of interested regions is improved.Convolutional neural network in reconstruction or predictive architectures is added in feature reconstruction loss to improve the detection accuracy of video abnormal behavior detection.The experimental results show that the proposed method can effectively improve the detection performance of video anomaly behaviors by 2.86%,2.54%and 0.56%on the Avenue dataset,UCSD-Ped1 dataset and UCSD-Ped2 dataset,respectively.关键词
视频异常行为检测/掩蔽卷积/外部注意力/自监督学习Key words
video anomaly behavior detection/masked convolution/external attention/self-supervised learning分类
计算机与自动化引用本文复制引用
邝永年,王丰,丁克..基于掩蔽卷积及外部注意力的视频异常行为检测算法[J].广东工业大学学报,2025,42(3):130-136,7.基金项目
国家自然科学基金资助项目(61901124) （61901124）
广东省自然科学基金资助项目(2021A1515012305) （2021A1515012305）
广东省研究生创新教育创新计划项目(2023JGXM_048) （2023JGXM_048）
