现代电子技术2025，Vol.48Issue(6)：136-146,11.DOI:10.16652/j.issn.1004-373x.2025.06.021
面向人体异常行为识别的FDS-ABPG-GoogLeNet模型研究
Research on FDS-ABPG-GoogLeNet model for human abnormal behavior recognition
摘要
Abstract
With the exacerbation of population aging,the identification technology of abnormal behaviors in the elderly has become a critical issue urgently needing to be addressed in the healthcare field.The current abnormal behavior recognition algorithm is faced with a challenge,that is,it cannot ensure the recognition accuracy and computational efficiency of the model while recognizing various abnormal behaviors.To address this issue,the FDS-ABPG-GoogLeNet model is proposed.In this model,three improved Inception modules at different levels are incorporated,and they are connected in parallel in both deep and shallow network structures.The residual structure is introduced in the middle structure,which significantly improves the computational efficiency and recognition accuracy of the network by means of the feature fusion.In order to solve the problem of single action in abnormal behavior data set,a dataset containing multiple abnormal actions is self built.By graphically processing one-dimensional action time series data in two dimensions,it makes it easier to extract behavioral action features.The experimental results demonstrate that the proposed FDS-ABPG-GoogLeNet model can realize an accuracy,senstivity,and specificity of 99.40%,99.49%,and 99.93%,respectively.关键词
异常行为识别/Inception模块/残差结构/特征融合/特征提取/卷积神经网络Key words
abnormal behavior recognition/Inception module/residual structure/feature fusion/feature extraction/convolutional neural network分类
电子信息工程引用本文复制引用
李一帆,李聪聪,李亚南,王斌..面向人体异常行为识别的FDS-ABPG-GoogLeNet模型研究[J].现代电子技术,2025,48(6):136-146,11.基金项目
河北省教育厅科学研究重点项目(ZD2021056) （ZD2021056）
河北省高等学校科学研究项目(203777119D) （203777119D）
2023河北省引进海外留学人员计划(C20230333) （C20230333）
