Research Progress of Deep Learning Methods in Two-dimensional Human Pose Estimation

Abstract

The task of human pose estimation is to locate and detect the key points of human body in images or videos.It has always been one of the hot research directions in the field of computer vision,and it is also a key step for computers to understand human actions.In recent years,it has wide application for predicting the poses of two-dimensional human body key points in images and videos.Using the powerful image feature extraction capabilities of deep learning,two-dimensional human pose estimation has been improved in robustness,accuracy,and processing time,and the performance effect is far beyond traditional methods.According to the different number of objects in the two-dimensional human body pose,it can be divided into single-person and multi-person pose estimation methods.For single-person pose estimation,according to the different representations of the extracted key points,coordinate regression methods based on the direct prediction of human coordinate points and heat map detection methods based on predicting the Gaussian distribution of human key points can be used.In multi-person pose estimation,it is divided into the top-down method which solves the process from multiple people to a single person,and a bottom-up method that directly deals with the key points of multiple people.Based on the existing estimation methods of human body posture,this paper analyzes the internal mechanism of the network structure,analyzes the commonly used datasets and evaluation indicators,and elaborates the current problems and future development trends.

Keywords

• two-dimensional human pose estimation|deep learning|single-person pose estimation|multi-person pose estimation|evaluation metrics