Stacked Hourglass Network with Additional Skip Connection for Human Pose Estimation

Abstract

The human pose estimation is a problem of localizing human joints in a single image, and that is still a challenge in the field of computer vision. The hourglass network has been used in many researches to achieve good performance in human pose estimation problems. For human pose estimation problem, not only high-level features but also low-level features are important for understanding the whole human body. However, the vanilla hourglass network has the problem of passing only high-level features to the next stack. Therefore, we propose a network structure that can solve the problems of the vanilla hourglass by using an additional skip connection. The proposed skip connection improves network performance by passing relative low-level features to the next stack. In addition, the skip connection is a simple element-wise Sum operation, so there is no increase in the number of parameters. In this work, we use the well-known human pose estimation data set, MPII, to evaluate the proposed method. We conducted experiments to evaluate the objective performance of the proposed method, and it was confirmed through this evaluation that the proposed method improves the performance of human pose estimation of the vanilla hourglass network.