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.