计算机工程与科学2025，Vol.47Issue(1)：130-139,10.DOI:10.3969/j.issn.1007-130X.2025.01.014
改进注意力混合自动编码器视频异常检测研究
Video anomaly detection with improved attention hybrid auto-encoder
摘要
Abstract
Video anomaly detection is one of the important research areas in computer vision,widely applied in fields such as transportation and public safety.However,the current field of video anomaly detection faces issues such as susceptibility to noise interference in individual prediction models and gen-eralization anomalies in individual reconstruction models.To address these problems,a video anomaly detection method combining reconstruction and prediction models is proposed.A reconstruction network with an attention mechanism and a memory enhancement module is trained on normal optical flow data.The reconstructed optical flow and original video frames are then simultaneously input into a future frame prediction network,where the reconstructed optical flow serves as a conditional aid to assist the frame prediction network in better generating future frames.To extract more effective features,a resid-ual convolutional attention module(SRCAM)is proposed to facilitate the reconstruction and prediction networks in effectively learning feature representations of latent spaces at both global and local levels,thereby enhancing the model's ability to detect anomalous events in videos and improving its robustness.Extensive experimental evaluations on two commonly used video anomaly detection datasets,UCSD Ped2 and CUHK Avenue,demonstrate the effectiveness of the proposed method.关键词
视频异常检测/注意力机制/流重构/帧预测/自动编码器Key words
video anomaly detection/attention mechanism/stream reconstruction/frame prediction/auto-encoder分类
信息技术与安全科学引用本文复制引用
陈兆波,张琳,马晓轩..改进注意力混合自动编码器视频异常检测研究[J].计算机工程与科学,2025,47(1):130-139,10.基金项目
北京市教育科学"十三五"规划重点课题(CHAA19081) （CHAA19081）
