PoseAnalyser: A Survey on Human Pose Estimation

Human pose estimation is the process of detecting the body keypoints of a person and can be used to classify different poses. Many researchers have proposed various ways to get a perfect 2D as well as a 3D human pose estimator that could be applied for various types of applications. This paper is a review of all the state-of-the-art architectures based on human pose estimation, the papers referred were based on the types of computer vision and machine learning algorithms, such as feed-forward neural networks, convolutional neural networks (CNN), OpenPose, MediaPipe, and many more. These different approaches are compared on various parameters, like the type of dataset used, the evaluation metric, etc. Different human pose datasets, such as COCO and MPII activity datasets with keypoints, as well as specific application-based datasets, are reviewed in this survey paper. Researchers may use these architectures and datasets in a range of domains, which are also discussed. The paper analyzes several approaches and architectures that can be used as a guide for other researchers to assist them in developing better techniques to achieve high accuracy. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.