Hand pose estimation based on improved NSRM network

Abstract

AbstractHand pose estimation is the basis of dynamic gesture recognition. In vision-based hand pose estimation, the performance of hand pose estimation is affected due to the high flexibility of hand joints, local similarity and severe occlusion among hand joints. In this paper, the structural relations between hand joints are established, and the improved nonparametric structure regularization machine (NSRM) is used to achieve more accurate estimation of hand pose. Based on the NSRM network, the backbone network is replaced by the new high-resolution net proposed in this paper to improve the network performance, and then the number of parameters is decreased by reducing the input and output channels of some convolutional layers. The experiment of hand pose estimation is carried out by using public dataset, the experimental results show that the improved NSRM network has higher accuracy and faster inference speed for hand pose estimation.