计算机工程2025，Vol.51Issue(2)：278-288,11.DOI:10.19678/j.issn.1000-3428.0068375
基于轻量级高分辨率网络的人体姿态估计算法
Human Pose-Estimation Algorithm Based on Lightweight High-Resolution Network
摘要
Abstract
Human pose estimation is widely used in multiple fields,including sports fitness,gesture control,unmanned supermarkets,and entertainment games.However,pose-estimation tasks face several challenges.Considering the current mainstream human pose-estimation networks with large parameters and complex calculations,LitePose,a lightweight pose-estimation network based on a high-resolution network,is proposed.First,Ghost convolution is used to reduce the parameters of the feature extraction network.Second,by using the Decoupled Fully Connected(DFC)attention module,the dependence relationship between pixels in the far distance space position is better captured and the loss in feature extraction due to decrease in parameters is reduced.The accuracy of human pose keypoint regression is improved,and a feature enhancement module is designed to further enhance the features extracted by the backbone network.Finally,a new coordinate decoding method is designed to reduce the error in the heatmap decoding process and improve the accuracy of keypoint regression.LitePose is validated on the human critical point detection datasets COCO and MPII and compared with current mainstream methods.The experimental results show that LitePose loses 0.2％accuracy compared to the baseline network HRNet;however,the number of parameters is less than one-third of the baseline network.LitePose can significantly reduce the number of parameters in the network model while ensuring minimal accuracy loss.关键词
人体姿态估计/高分辨率网络/轻量化网络/GhostV2/坐标解码Key words
human pose estimation/high-resolution network/lightweight network/GhostV2/coordinate decoding分类
信息技术与安全科学引用本文复制引用
刘圣杰,何宁,王鑫,于海港,韩文静..基于轻量级高分辨率网络的人体姿态估计算法[J].计算机工程,2025,51(2):278-288,11.基金项目
国家自然科学基金(62272049,62236006) （62272049,62236006）
北京市教委重点项目(KZ201911417048) （KZ201911417048）
科技创新2030重大项目-"新一代人工智能"(2018AAA0100800) （2018AAA0100800）
北京市教委科技项目(KM202111417009,KM201811417005). （KM202111417009,KM201811417005）
