Efficient Human Pose Estimation in Hierarchical Context
Feng Zhang, Xiatian Zhu, Mao Ye · IEEE Access · 2019
Most existing human pose estimation methods focus on enhancing the accuracy performance alone while ignoring the critical model efficiency issue. This dramatically limits their scalability and deployability in large-scale applications. In this paper, we consider the under-studied model efficiency problem in pose estimation. We demonstrate the advantages and potential of hierarchical context learning in the convolutional neural network. Specifically, we formulate a novel hierarchical context network (HCN) architecture that can be trained and deployed efficiently while achieving competitive model generalization capability. This is achieved by progressively forming and imposing multi-granularity context information during the pose regression learning process in a coarse-to-fine manner. The extensive comparative evaluations validate the superiority of the proposed HCN over a wide variety of the state-of-the-art human pose estimation models on two challenging benchmarks: MPII and LSP.