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Efficient Human Pose Estimation in Hierarchical Context
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DOI：10.1109/ACCESS.2019.2902330.png)
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摘要
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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.
Keyword:
Fast deployment
human pose estimation
hierarchical context
model cost-effectiveness
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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