基于卷积神经网络的2D人体姿态估计综述

Overview of 2D human pose estimation based on convolutional neural network

随着深度学习的快速发展,2D人体姿态估计作为其他计算机视觉任务的研究基础,其检测速度和精度对后续应用落地具有实际意义。对近年来基于卷积神经网络的2D人体姿态估计的方法进行梳理介绍,将现有方法分为人体检测关节点回归融合算法和人体关节点检测聚类算法,同时对当前的主流数据集及其评价准则进行总结,最后对2D人体姿态估计当前所面临的困难以及未来的发展趋势做以阐述,为姿态估计相关研究提供一些参考。

With the rapid development of deep learning,2D human pose estimation is used as the research basis for other computer vision tasks,and its detection speed and accuracy have practical significance for subsequent applications.This paper introduces the methods of 2D human pose estimation based on convolutional neural networks in recent years.The existing methods are divided into human body detection combined with joint point regression algorithm and human body joint point detection clustering algorithm.At the same time,the current mainstream datasets and the evaluation criteria are summarized,and finally the current difficulties and future development trends of 2D human pose estimation are explained,which provides some references for related research on pose estimation.