现代电子技术2025，Vol.48Issue(9)：8-14,7.DOI:10.16652/j.issn.1004-373x.2025.09.002
基于上下文信息和MLE机制的视频异常检测算法
Video anomaly detection algorithm based on contextual information and MLE mechanism
摘要
Abstract
The existing video anomaly detection methods lack autonomous selectivity for foreground objects,which increases the model's sensitivity to irrelevant information such as background,and thus leads to reconstruction or prediction errors proportional to the number of foreground objects,resulting in false alarms,so a video anomaly detection algorithm based on contextual information and maximum local error(MLE)mechanism is proposed.In the algorithm,a detection framework based on generative adversarial networks(GANs)is designed and a generator model SSPCAB-UNet is proposed,which enhances the model's ability to understand local features and global contextual information by adding a self-supervised predictive convolutional attentive block(SSPCAB)to the UNet,so as to reduce the attention to irrelevant information,and reduce the possibility of false alarms.In addition,the MLE mechanism is used to focus on the prediction of local anomalous regions,so as to alleviate the large prediction errors caused by the increased number of foreground objects.The false alarm due to lack of autonomous selectivity of foreground objects can be reduced effectively by the synergy of the dual modules.The method achieves detection accuracies of 87.5%,98.2%and 75.4%on the datasets CUHK Avenue,UCSD Ped2 and Shanghai Tech,respectively,which validates the effectiveness of the proposed model.关键词
异常检测/自监督预测卷积关注块/最大局部误差机制/自动编码器/深度学习/生成对抗网络Key words
anomaly detection/SSPCAB/MLE mechanism/autoencoder/deep learning/GAN分类
信息技术与安全科学引用本文复制引用
焦雪,高立青..基于上下文信息和MLE机制的视频异常检测算法[J].现代电子技术,2025,48(9):8-14,7.基金项目
国家自然科学基金项目(72004154) （72004154）
