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MobiRFPose: Portable RF-Based 3D Human Pose Camera
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AI
DOI：10.1109/TMM.2023.3314979.png)
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摘要
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Existing RF-based human pose estimation methods usually require intensive computations and cannot meet the real-time processing and portability requirements for mobile devices. To tackle the limitation, in this article, we introduce a lightweight RF-based pose estimation model, i.e., MobiRFPose, to construct the portable RF-based pose camera. Different from traditional optical-based cameras, the RF-based camera does not capture visual information, which means the privacy-preserving characteristic. Specifically, we only utilize a horizontal antenna array to transceive RF signals, then estimate the human locations on the RF signal heatmap and crop the human location regions, and finally estimate the fine-grained human poses based on the cropped small RF signal heatmaps. To evaluate the performance, we compare MobiRFPose with state-of-the-art methods. Experimental results demonstrate that MobiRFPose can achieve accurate 3D human pose estimation with fewer parameters and computations. We also test the trained MobiRFPose model using mobile computing devices, where the model structures and parameters only take up 268 KB and 3226 KB of disk space, and MobiRFPose can achieve 66 FPS processing speed. The pose estimation error is 11.05 cm in the case of a single person and 11.29 cm in the case of multiple people. All experimental results indicate that our proposed method can construct a portable RF camera to estimate human poses accurately.
Keyword:
RF signals
Three-dimensional displays
Antenna arrays
Pose estimation
Radio frequency
Cameras
Computational modeling
Human pose estimation
lightweight model
wireless sensing
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IF:
9.7
论文数:
4.5K
被引数:
2.4W
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