In order to solve the problem of information loss caused by using only the last feature layer and ignoring the connection between convolution layers in human pose estimation based on convolution neural network

a multilevel dense block hourglass network is proposed. Dense network has the characteristic of interconnecting each layer

which can greatly reduce the information loss in stacked hourglass network and improve the recognition precision. Experiments show that the evaluation results on FLIC and MPII are prior to stacked hourglass networks which are widely used at present.