Multi-Branch Network for Small Human Pose Estimation

The task of 2D human pose estimation aims to obtain the position of the body’s articulation points in picture, it is the basis for many other tasks, but the current human pose estimation network has shortcomings when dealing with small objects. Due to the ambiguity caused by inadequate expansion, a small human object contains insufficient semantic information. Therefore, the prediction of coordinate points becomes imprecise. In this paper, to address the problem of small human pose estimation, we present a novel network structure called Multi-Branch Network (MBN), consisting of three modules: Multi-Branch Expansion Module (MBEM), Multi-Branch Downsample Module (MBDM), and Refine Module (RM). MBEM reduces the input bias before the image enters the backbone network. MBDM adds an extra downsampling branch to obtain richer semantic information. RM locates hard joints by the refined operation. The experimental studies on COCO benchmark show that our approach gains noteworthy enhancements on the state-of-the-art single-stage models of ResNet and RSN.