Dense-connected Stacked Hourglass Networks for Human Pose Estimation

Abstract

The main idea of this project is to try to improve the accuracy of human pose estimation in previous models. The new model proposed is based on the Stacked Hourglass Network with new structures added. The new structures ensured that the preservation of features of the original data by adding connections across the network, which we refer to as a Dense-connected Stacked Hourglass network, and we expected the new structure and the feature preserved could be helpful in the later stages because the Stacked Hourglass network pools down to very low resolution, during which important information may be lost. The data sets used in the project are MPII Human Pose and FLIC (Frames Labelled in Cinema). The final results show that the proposed architecture is able to improve the estimation accuracy to certain extend in identifying head, wrist and hip, while further studies on the architecture and improvements are still required.