ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation  

作　　者：Mi Zhou Rui Liu Pengfei Yi Dongsheng Zhou 

机构地区：[1]National and Local Joint Engineering Laboratory of Computer Aided Design,School of Software Engineering,Dalian University,Dalian,116622,China [2]School of Computer Science and Technology,Dalian University of Technology,Dalian,116024,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第8期2093-2109,共17页工程与科学中的计算机建模（英文）

基　　金：supported in part by the Key Program of NSFC (Grant No.U1908214);Special Project of Central Government Guiding Local Science and Technology Development (Grant No.2021JH6/10500140);Program for the Liaoning Distinguished Professor,Program for Innovative Research Team in University of Liaoning Province (LT2020015);Dalian (2021RT06)and Dalian University (XLJ202010);the Science and Technology Innovation Fund of Dalian (Grant No.2020JJ25CY001);Dalian University Scientific Research Platform Project (No.202101YB03).

摘　　要：Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

关 键 词：Multi-view multi-person pose estimation attention mechanism computer vision 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]