基于深度学习的二维人体姿态估计算法综述

二维人体姿态估计作为人体动作识别的基础,随着深度学习和神经网络的流行已经成为备受学者关注的研究热点.与传统方法相比,深度学习能够得到更深层图像特征,对数据的表达更准确,因此已成为研究的主流方向.本文主要介绍了二维人体姿态估计算法,首先根据检测人数分为单人姿态估计与多人姿态估计两类,其次对单人姿态估计分为基于坐标回归与基于热图检测的方法;对多人姿态估计可分为自顶向下(top-down)和自底向上(bottom-up)的方法.最后介绍了姿态估计常用数据集以及评价指标对部分多人姿态估计算法的性能指标进行了对比,并对人体姿态估计研究所面临的问题与发展趋势进行了阐述.

Overview on Two-dimensional Human Pose Estimation Methods Based on Deep Learning

As the basis of human motion recognition, two-dimensional human pose estimation has become a research hotspot with the popularity of deep learning and neural networks. Compared with traditional methods, deep learning can achieve deeper image features and express the data more accurately, thus becoming the mainstream of research. This study mainly introduces two-dimensional human pose estimation algorithms. Firstly, according to the number of people detected, the algorithms are divided into two categories for single-person and multi-person pose estimation. Secondly, the single-person pose estimation methods are divided into two groups based on coordinate regression and heat map detection. Multi-person poses can be estimated by top-down and bottom-up methods. Finally, the study introduces commonly used data sets and evaluation indexes of human pose estimation and compares the performance indexes of some multi-person pose estimation algorithms. It also expounds on the challenges and development trends of human pose estimation.