论文标题
对自上而下的人姿势估计方法的调查
A survey of top-down approaches for human pose estimation
论文作者
论文摘要
二维图像中的人类姿势估计视频是计算机视觉问题的热门话题，因为它的巨大好处和潜在的改善人类生活的应用，例如行为识别，运动捕获和增强现实，训练机器人和运动跟踪。许多用深度学习实施的最先进的方法已经解决了一些挑战，并在人类姿势估计领域带来了巨大的结果。方法分为两种：两步框架（自上而下的方法）和基于零件的框架（自下而上的方法）。两步框架首先结合了一个人检测器，然后独立估算每个盒子内的姿势，检测图像中的所有身体部位并关联属于不同人的部分，在基于部分的框架中进行。本文旨在为新移民提供对基于深度学习方法的2D图像的广泛审查，以识别人们的姿势，该图像仅着眼于自2016年以来的自上而下的方法。本文的讨论介绍了重要的检测器和估计量，这取决于数学背景，取决于挑战和局限性，基于挑战和局限性，基准数据集，评估指标和方法之间的方法。
Human pose estimation in two-dimensional images videos has been a hot topic in the computer vision problem recently due to its vast benefits and potential applications for improving human life, such as behaviors recognition, motion capture and augmented reality, training robots, and movement tracking. Many state-of-the-art methods implemented with Deep Learning have addressed several challenges and brought tremendous remarkable results in the field of human pose estimation. Approaches are classified into two kinds: the two-step framework (top-down approach) and the part-based framework (bottom-up approach). While the two-step framework first incorporates a person detector and then estimates the pose within each box independently, detecting all body parts in the image and associating parts belonging to distinct persons is conducted in the part-based framework. This paper aims to provide newcomers with an extensive review of deep learning methods-based 2D images for recognizing the pose of people, which only focuses on top-down approaches since 2016. The discussion through this paper presents significant detectors and estimators depending on mathematical background, the challenges and limitations, benchmark datasets, evaluation metrics, and comparison between methods.
