Human pose estimation aims at predicting the poses of human body parts in images or videos. Since pose motions are often driven by some specific human actions, knowing the body pose of a human is critical for action recognition. This survey focuses on recent progress of human pose estimation and its application to action recognition. We attempt to provide a comprehensive review of recent bottom-up and top-down deep human pose estimation models, as well as how pose estimation systems can be used for action recognition. Thanks to the availability of commodity depth sensors like Kinect and its capability for skeletal tracking, there has been a large body of literature on 3D skeleton-based action recognition, and there are already survey papers such as [1] about this topic. In this survey, we focus on 2D skeleton-based action recognition where the human poses are estimated from regular RGB images instead of depth images. We summarize the performance of recent action recognition methods that use pose estimated from color images as input, then show that there is much room for improvements in this direction.

人体姿势估计旨在预测图像或视频中人体部位的姿势。由于姿势运动通常是由某些特定的人类动作驱动的，因此了解人的身体姿势对于动作识别至关重要。这项调查的重点是人体姿势估计的最新进展及其在动作识别中的应用。我们试图提供对最新的自下而上和自上而下的深度人体姿势估计模型的全面综述，以及姿势估计系统如何用于动作识别。由于有诸如Kinect之类的商品深度传感器的可用性及其用于骨骼跟踪的功能，因此已有大量关于基于3D骨骼的动作识别的文献，并且已经有关于该主题的调查论文，例如[1]。在这项调查中 我们专注于基于2D骨骼的动作识别，其中人的姿势是根据常规RGB图像而不是深度图像估算的。我们总结了使用从彩色图像估计的姿势作为输入的最新动作识别方法的性能，然后表明在这个方向上还有很大的改进空间。