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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2020> 29(1): 110-119
Wenxia Bao, Yaping Yang, Dong Liang, Ming Zhu. Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2020, 29(1): 110-119. doi: 10.15918/j.jbit1004-0579.18151
Citation: Wenxia Bao, Yaping Yang, Dong Liang, Ming Zhu. Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2020, 29(1): 110-119.doi:10.15918/j.jbit1004-0579.18151
shu

Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation

doi:10.15918/j.jbit1004-0579.18151

  • Key Laboratory Intelligent Computing and Signal Processing of the Ministry of Education, Anhui University, Hefei 230039, China

  • Received Date:2018-11-16
    Abstract
  • A multi-residual module stacked hourglass network (MRSH) was proposed to improve the accuracy and robustness of human body pose estimation. The network uses multiple hourglass sub-networks and three new residual modules. In the hourglass sub-network, the large receptive field residual module (LRFRM) and the multi-scale residual module (MSRM) are first used to learn the spatial relationship between features and body parts at various scales. Only the improved residual module (IRM) is used when the resolution is minimized. The final network uses four stacked hourglass sub-networks, with intermediate supervision at the end of each hourglass, repeating high-low (from high resolution to low resolution) and low-high (from low resolution to high resolution) learning. The network was tested on the public datasets of Leeds sports poses (LSP) and MPII human pose. The experimental results show that the proposed network has better performance in human pose estimation.
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