A Parameter Efficient Human Pose Estimation Method Based on Densely Connected Convolutional Module

Abstract

In this paper, we propose a novel densely connected convolutional module (DCCM)-based convolutional neural network for human pose estimation, which can achieve higher parameter efficiency compared to the state-of-the-art works. Although existing methods for human pose estimation have achieved considerable accuracy, the number of required model parameters and computation complexity are relatively high. To solve this problem, we propose to use a DCCM as the basic unit of the neural network. For each layer of DCCM, feature maps that all preceding layers produce are concatenated as its input, and its own output feature maps are delivered to each subsequent layer. The experimental results on the MPII human pose data set and LSP data set show that our method can get comparable performance, while it requires less parameters, which means higher parameter efficiency can be achieved. Furthermore, we explore that how different configurations of the proposed network structure can affect the accuracy of human pose estimation.