Vision-Based Human Pose Estimation via Deep Learning: A Survey
It serves as introductory material for beginners and supplementary material for advanced researchers in computer vision, but it is incremental as a survey.
This survey paper tackles the lack of a comprehensive review of deep learning-based human pose estimation methods by providing an up-to-date overview of 2D and 3D approaches, their applications, challenges, and research trends.
Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human-computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in the literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.