Survey on Two-Dimensional Human Pose Estimation of Deep Learning

Abstract

In recent years, as a hot spot in the field of computer vision, human pose estimation has broad application prospects in video surveillance, human-computer interaction, and intelligent campus. With the rapid development of neural networks, the use of deep learning methods for 2D human pose estimation, compared with traditional methods that require manual setting of features, can more fully extract image information, and obtain more robust characteristics, so deep learning based methods have become the mainstream of 2D human pose estimation algorithm research. However, deep learning is still developing, there are still problems such as large training scale, and researchers mainly start with designing networks and training methods to improve the human pose estimation algorithm. Fristly, the two-dimensional human pose estimation is divided into two categories: single person and multiple persons. Secondly, single-person pose estimation is divided into coordinate regression and heat map detection according to different ground truth types, and multi-person pose estimation is divided into two-step and single-step method according to different algorithm steps, to summarize and classify the advanced algorithms in recent years, and their advantages and disadvantages as well as scope of application are analyzed. Then, the international standard datasets and the corresponding evaluation indices are introduced, and some classical algorithms are compared experimentally. Finally, the current research problems and the future development trends are summarized.

Keywords

• human pose estimation
• deep learning
• neural networks
• joints detection