Human pose estimation and its application to action recognition: A survey

Abstract

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.