湖北民族大学学报（自然科学版）2024，Vol.42Issue(3)：368-374,7.DOI:10.13501/j.cnki.42-1908/n.2024.06.025
基于时间序列分配和多频率分量压缩的人体行为识别
Human Action Recognition Based on Time Series Allocation and Multi-frequency Component Compression
摘要
Abstract
In order to address the issues of time-series feature extraction and feature compression in three dimensional convolutional neural network(3D-CNN)-based human action recognition,an improved network stronger 3D-CNN-based approach for skeleton-based action recognition(S-PoseConv3D)was proposed.The network effectively improved the performance of the original PoseConv3D by introducing the time series allocation strategy(TSAS)and the multi-frequency component feature compression fusion(M3F).TSAS dynamically adjusted the weights of different time series in the feature extraction process,and calculating the weights of the time series to enhance the model′s ability to capture the spatial-temporal features of human behaviors.Meanwhile,the M3F utilized the discrete cosine transform to compress and fuse the multi-frequency components in the feature map,thereby retaining the spatial and temporal features of human behaviors and reducing feature loss due to global pooling.The network was experimented on the dataset built on top of gymnastic videos(FineGYM)and Nanyang university of technology skeleton behavior recognition dataset 60 classification extended subset(NTU60-XSub),and the results showed that the improved network improved the mean top-1 accuracy by 3.93%on the FineGYM dataset compared to the original PoseConv3D.It also demonstrated significant performance improvement on the NTU60-XSub dataset,which indicated higher accuracy and robustness,and it was well-suited for application in the field of human behavior recognition.This network can be applied in such fields as security monitoring and human-computer interaction.关键词
行为识别/三维卷积神经网络/时空特征融合/注意力机制/离散余弦变换Key words
action recognition/3D-CNN/spatial-temporal feature fusion/attention mechanisms/discrete cosine transformations分类
信息技术与安全科学引用本文复制引用
佘本杰,朱彦敏,宋健,华健..基于时间序列分配和多频率分量压缩的人体行为识别[J].湖北民族大学学报（自然科学版）,2024,42(3):368-374,7.基金项目
国家自然科学基金项目(52374155) （52374155）
安徽理工大学研究生创新基金项目(2023cx2132). （2023cx2132）
