计算机工程与应用2024，Vol.60Issue(17)：216-223,8.DOI:10.3778/j.issn.1002-8331.2305-0210
改进重建和预测网络的人体异常行为检测方法
Improve Human Abnormal Behavior Detection Method of Reconstruction and Prediction Network
摘要
Abstract
In the detection of human abnormal behavior,in order to make full use of action and spatio-temporal feature information,a detection method of human abnormal behavior based on reconstruction and prediction network is proposed.The network structure in this method consists of a reconstruction sub-network and a video prediction sub-network,in which the reconstruction sub-network adopts a self-encoder structure and reconstructs the next frame with continuous video frames as input.The prediction sub-network adopts the encoder and decoder structure based on 3D convolution as the backbone of the network,and predicts the subsequent video frames by inputting a series of video frame pictures.In addi-tion,in order to make the reconstructed sub-network pay more attention to the action characteristics of human behavior,Zhan Sen-Shannon divergence(JSD)is used to calculate the difference between the reconstructed frame and the original frame,and the regularization constraint of temporal and spatial consistency is added to the prediction sub-network.The experimental results on three datasets,UCSDped2,Avenue and ShanghaiTech,show that this method has better perfor-mance on AUC index than other video human abnormal behavior detection methods,and it reaches 97.3%,91.1%and 82.6%in UCSDped2,Avenue and ShanghaiTech datasets respectively.关键词
异常行为检测/自编码器/3D卷积/时空一致性Key words
abnormal behavior detection/autoencoders/3D convolution/spatiotemporal consistency分类
信息技术与安全科学引用本文复制引用
张红民,庄旭,郑敬添..改进重建和预测网络的人体异常行为检测方法[J].计算机工程与应用,2024,60(17):216-223,8.基金项目
重庆市自然科学基金面上项目(cstc2021jcyj-msxmX0525). （cstc2021jcyj-msxmX0525）
