Optimized convolutional pose machine for 2D hand pose estimation

Abstract

Hand pose estimation is a challenging task owing to the high flexibility and serious self-occlusion of the hand. Therefore, an optimized convolutional pose machine (OCPM) was proposed in this study to estimate the hand pose accurately. Traditional CPMs have two components, a feature extraction module and an information processing module. First, the backbone network of the feature extraction module was replaced by Resnet-18 to reduce the number of network parameters. Furthermore, an attention module called the convolutional block attention module (CBAM) is embedded into the feature extraction module to enhance the information extraction. Then, the structure of the information processing module was adjusted through a residual connection in each stage that consist of a series of continuous convolutional operations, and requires a dense fusion between the output from all previous stages and the feature extraction module. The experimental results on two public datasets showed that the OCPM network achieved excellent performance.