文献类型：期刊文献

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

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China; [2] Smart City College, Beijing Union University, Beijing, China; [3] University of Chinese Academy of Sciences, Beijing, 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

年份：2021

起止页码：682-687

外文期刊名：ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval

收录：EI(收录号：20213810902932)

语种：英文

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. ? 2021 ACM.