论文标题
基于深度学习的人类姿势估计：一项调查
Deep Learning-Based Human Pose Estimation: A Survey
论文作者
论文摘要
人类姿势估计旨在从输入数据（例如图像和视频）中定位人体部位并建立人体代表性（例如，身体骨架）。在过去的十年中，它引起了人们的关注，并已在包括人类计算机互动，运动分析，增强现实和虚拟现实在内的广泛应用中使用。尽管最近开发的基于深度学习的解决方案在人类姿势估计中取得了高度的表现，但由于训练数据，深度歧义和遮挡不足，仍然存在挑战。该调查论文的目的是通过系统分析和基于这些解决方案的输入数据和推理程序对2D和3D姿势估算的最新基于深度学习的解决方案进行全面审查。自2014年以来，本调查涵盖了250多个研究论文。此外，包括2D和3D人姿势估计数据集和评估指标。总结并讨论了对流行数据集的审查方法的定量性能比较。最后，涉及的挑战，应用程序和未来的研究方向得出了结论。提供了定期更新的项目页面：\ url {https://github.com/zczcwh/dl-hpe}
Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 250 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. A regularly updated project page is provided: \url{https://github.com/zczcwh/DL-HPE}
