Researches Advanced in Human Pose Estimation Based on Deep Learning

Human pose estimation has always been a popular research topic in the discipline of computer vision, which aims to predict the spatial position of the human body’s key points (parts/joints) from a given image or video. Benefiting from the powerful feature representation ability of the convolutional neural networks, the pose estimation methods based on the deep learning have become the mainstream framework of this task, which obtain the human pose estimation results by extracting the human pose feature information at different scales with specific neural networks and using the corresponding methods to process it. This paper, based on the depth study of human body posture estimation can be divided into single-task and multi-person missions. The time and method of correlation are classified and summarized, respectively from the network architecture, algorithm and feature extraction of performance that are analyzed. The current problems existing in the research field and methods are summarized, and the research prospect for the future has been prospected.