Single Person Pose Estimation: A Survey
It provides a structured taxonomy for researchers and practitioners in computer vision, but it is incremental as it reviews existing work without introducing new methods.
This survey summarizes the evolutionary path of single-person pose estimation methods, focusing on deep learning models and covering all components of the pipeline, including data augmentation, model architecture, and evaluation metrics.
Human pose estimation in unconstrained images and videos is a fundamental computer vision task. To illustrate the evolutionary path in technique, in this survey we summarize representative human pose methods in a structured taxonomy, with a particular focus on deep learning models and single-person image setting. Specifically, we examine and survey all the components of a typical human pose estimation pipeline, including data augmentation, model architecture and backbone, supervision representation, post-processing, standard datasets, evaluation metrics. To envisage the future directions, we finally discuss the key unsolved problems and potential trends for human pose estimation.