Joint usage of global and local attentions in hourglass network for human pose estimation

Human pose estimation is a challenging research task in the field of computer vision. The current mainstream work has made great progress in pose estimation, but these works still do not pay enough attention to the negative impact of background on human pose estimation. In this work, we propose a human pose estimation framework characterized by the joint usage of both global and local attention module in an hourglass backbone network. The global attention module aims to reduces the negative impact of background. The local attention module is designed to help refine each joint. We tested our method on two benchmark datasets for human pose estimation, and the experimental results show that the proposed model is superior to current mainstream algorithms.