FaSRnet:a feature and semantics refinement network for human pose estimation  

作　　者：Yuanhong ZHONG Qianfeng XU Daidi ZHONG Xun YANG Shanshan WANG 

机构地区：[1]School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China [2]Bioengineering College of Chongqing University,Chongqing University,Chongqing 400044,China [3]School of Information Science and Technology,University of Science and Technology of China,Hefei 230039,China [4]Institutes of Physical Science and Information Technology,Anhui University,Hefei 230039,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2024年第4期513-526,共14页信息与电子工程前沿（英文版）

基　　金：supported by the National Key Research and Development Program of China(Nos.2021YFC2009200 and 2023YFC3606100);the Special Project of Technological Innovation and Application Development of Chongqing,China(No.cstc2019jscx-msxmX0167)。

摘　　要：Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue.Currently,most methods explore temporal consistency through refinements of the final heatmaps.The heatmaps contain the semantics information of key points,and can improve the detection quality to a certain extent.However,they are generated by features,and feature-level refinements are rarely considered.In this paper,we propose a human pose estimation framework with refinements at the feature and semantics levels.We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions.An attention mechanism is then used to fuse auxiliary features with current features.In terms of semantics,we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps.The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018,and the results demonstrate the effectiveness of our method.

关 键 词：Human pose estimation Multi-frame refinement Heatmap and offset estimation Feature alignment Multi-person 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]