文献类型：会议论文

英文题名：Human Pose Estimation Based on Attention Multi-resolution Network

作者：Zhang, Congcong[1];He, Ning[2];Sun, Qixiang[1];Yin, Xiaojie[2];Lu, Ke[3]

第一作者：Zhang, Congcong

通讯作者：Zhang, CC[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Smart City Coll, Beijing, Peoples R China;[3]Univ Chinese Acad Sci, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：11th International Conference on Multimedia Retrieval (ICMR)

会议日期：NOV 16-19, 2021

会议地点：ELECTR NETWORK

语种：英文

外文关键词：Human pose estimation; attention mechanism; multi-resolution networks; feature fusion

摘要：Recently, multi-resolution neural networks, which combine features of different resolutions, have achieved good results in human pose estimation tasks. In this paper, we propose an attention-mechanism-based multi-resolution network, which adds an attention mechanism to the High-Resolution Network (HRNet) to enhance the feature representation of the network. It improves the ability of networks with different resolutions to extract key features from images, and causes the output to contain more effective multi-resolution representation information, so that the corresponding point positions of human joints can be estimated more accurately. Experiments on the MPII and COCO datasets, and verification on the MPII datasets, obtained an average accuracy of 90.3% under the PCKh@0.5 evaluation standard, and good results were also achieved on the COCO dataset (with an AP of 76.5). The experimental results show that our network model is effective in improving the accuracy of key point estimation in the human pose estimation task.