文献类型：期刊文献

英文题名：Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention

作者：Li, Xin[1];Guo, Yuxin[1];Pan, Weiguo[1];Liu, Hongzhe[1];Xu, Bingxin[1]

通讯作者：Pan, WG[1];Liu, HZ[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, 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 100101, 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;

年份：2023

卷号：13

期号：6

外文期刊名：APPLIED SCIENCES-BASEL

收录：;Scopus(收录号：2-s2.0-85152020596);WOS:【SCI-EXPANDED(收录号:WOS:000954332100001)】；

基金：This work was supported by the Beijing Natural Science Foundation (4232026); National Natural Science Foundation of China (grant nos. 62006020, 62272049, 62171042, 61871039, 62102033); Key Project of Science and Technology Plan of Beijing Municipal Education Commission (KZ202211417048); Academic Research Projects of Beijing Union University (ZK10202202); and the Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2020DZ02).

语种：英文

外文关键词：human pose estimation; attention mechanism; multi-scale feature extraction

摘要：Heatmap-based traditional approaches for estimating human pose usually suffer from drawbacks such as high network complexity or suboptimal accuracy. Focusing on the issue of multi-person pose estimation without heatmaps, this paper proposes an end-to-end, lightweight human pose estimation network using a multi-scale coordinate attention mechanism based on the Yolo-Pose network to improve the overall network performance while ensuring the network is lightweight. Specifically, the lightweight network GhostNet was first integrated into the backbone to alleviate the problem of model redundancy and produce a significant number of effective feature maps. Then, by combining the coordinate attention mechanism, the sensitivity of our proposed network to direction and location perception was enhanced. Finally, the BiFPN module was fused to balance the feature information of different scales and further improve the expression ability of convolutional features. Experiments on the COCO 2017 dataset showed that, compared with the baseline method YOLO-Pose, the average accuracy of the proposed network on the COCO 2017 validation dataset was improved by 4.8% while minimizing the amount of network parameters and calculations. The experimental results demonstrated that our proposed method can improve the detection accuracy of human pose estimation while ensuring that the model is lightweight.