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ZHANG Rui, GONG Hanqin, SONG Ruiyuan, et al. Through-wall human pose reconstruction and action recognition using four-dimensional imaging radar[J]. Journal of Radars, in press. doi: 10.12000/JR24132
Citation: ZHANG Rui, GONG Hanqin, SONG Ruiyuan, et al. Through-wall human pose reconstruction and action recognition using four-dimensional imaging radar[J]. Journal of Radars, in press. doi: 10.12000/JR24132
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Through-wall Human Pose Reconstruction and Action Recognition Using Four-dimensional Imaging Radar

DOI: 10.12000/JR24132

  • School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China

Funds:  The National Natural Science Foundation of China (62172381, 62201542)
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  • Corresponding author: CHEN Yan, eecyan@ustc.edu.cn
  • Received Date: 2024-07-01
  • Rev Recd Date: 2024-08-11
    • Available Online: 2024-08-15
    Abstract
  • Through-wall human pose reconstruction and behavior recognition have enormous potential in fields like intelligent security and virtual reality. However, existing methods for through-wall human sensing often fail to adequately model four-Dimensional (4D) spatiotemporal features and overlook the influence of walls on signal quality. To address these issues, this study proposes an innovative architecture for through-wall human sensing using a 4D imaging radar. The core of this approach is the ST2W-AP fusion network, which is designed using a stepwise spatiotemporal separation strategy. This network overcomes the limitations of mainstream deep learning libraries that currently lack 4D convolution capabilities, which hinders the effective use of multiframe three-Dimensional (3D) voxel spatiotemporal domain information. By preserving 3D spatial information and using long-sequence temporal information, the proposed ST2W-AP network considerably enhances the pose estimation and behavior recognition performance. Additionally, to address the influence of walls on signal quality, this paper introduces a deep echo domain compensator that leverages the powerful fitting performance and parallel output characteristics of deep learning, thereby reducing the computational overhead of traditional wall compensation methods. Extensive experimental results demonstrate that compared with the best existing methods, the ST2W-AP network reduces the average joint position error by 33.57% and improves the F1 score for behavior recognition by 0.51%.

     

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