Human Pose Estimation Based Pre-training Model and Efficient High-Resolution Representation

With the continuous development of computer vision technology, deep learning technology has been well applied in human pose estimation. However, the latest human pose estimation algorithms can not solve the problem of interdependence and occlusion between joints, so the key point detection in human pose estimation is still a challenging task. In this paper, a method based on masked pre-training model and lightweight SHNet combined method, random occlusion is performed on the key points of the human body in the picture, and the pre-training model is used to predict them, so that the problems of mutual occlusion and interdependence can be solved. Experiments show that the algorithm is effective in key point detection of MPII and COCO 2017 datasets.