Stacked mixed-scale networks for human pose estimation

Human pose estimation is an important problem in computer vision, which has been dominated by deep learning techniques in recent years. In this paper, we propose a novel model, named Mixed-Scale Dense Block, that exploits dilation convolution layers and dense concatenation connections to maximise the information flow through the block. Consequently, it captures the feature representation in different scales more effectively and efficiently. Comparing with the baseline method, Hourglass models, our model employs fewer learning parameters. Nevertheless, experiments demonstrate that the proposed model produces more accurate predictions. Meanwhile, our method achieves the comparable accuracy to state-of-the-art techniques. Especially in some indicators, our approach has better performance. In addition, this model is easy to implement and could be improved by most existing techniques that are adopted to promote the hourglass models.