Monocular 2D and 3D human pose estimation review

Abstract

Human pose estimation has been a significant task in computer vision in the last few decades and had a wide variety of applications in the scientific field and in people’s daily life. People usually use cameras to obtain RGB images or videos to compose an integrated dataset and then use different deep learning methods or some other combinations of methods to try to estimate the proper posture of the human body. Although scientists have thought of various kinds of technical ways to estimate human pose, they still cannot achieve 100 percent accuracy because of some unavoidable factors, for example, the complex environmental changes of everyday life, flexible body, and a variety of body shapes make prediction accuracy more difficult because it affects the confirmation of key points, and limitations of some methods. For better research, this review will focus on advanced technical methods, datasets, and metrics for 3D and 2D human pose estimation with single or multiple individuals.