Environment Adaptive RFID-Based 3D Human Pose Tracking With a Meta-Learning Approach
Environment Adaptive RFID-Based 3D Human Pose Tracking With a Meta-Learning Approach
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DOI：
10.1109/jrfid.2022.3140256
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发表时间：
2022
影响因子：
3.1
通讯作者：
Chao Yang;Lingxiao Wang;Xuyu Wang;S. Mao
中科院分区：
文献类型：
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作者：
Chao Yang;Lingxiao Wang;Xuyu Wang;S. Mao
近年来，随着射频传感技术的发展，基于射频的三维人体姿态估计引起了越来越多的关注。与基于摄像机的技术不同，射频传感具有保护用户隐私的独特优势。然而，由于室内无线信道的复杂性，训练有素的射频传感系统通常很难推广到新的环境中。针对基于射频识别(RFID)的三维人体骨骼跟踪系统，提出了一种环境自适应的解决方案。我们首先分析了基于RFID的传感系统在环境适应方面的挑战。在此基础上，提出了一种基于RFID的三维人体姿态跟踪的元学习方法，称为Meta-Ppose。该系统采用现成的RFID设备实施，可以很好地适应新的环境，只需很少的微调，从而大大简化了训练系统的部署。我们在不同的室内场景中进行了大量的实验，以验证Meta-pose系统的高适应性和准确性。
With the development of Radio-Frequency (RF) sensing techniques, RF based 3D human pose estimation has attracted increasing interest recently. Unlike video camera based techniques, RF sensing has the unique strength of preserving user privacy. However, due to the complex wireless channels indoors, a well-trained RF sensing system is usually hard to generalize to new environments. In this paper, we propose an environment adaptive solution for Radio-Frequency Identification (RFID) based 3D human skeleton tracking systems. We first analyze the challenges in environment adaptation for RFID based sensing systems. Following the analysis, we then propose a meta-learning approach for RFID-based 3D human pose tracking, termed Meta-Pose. The system is implemented with off-the-shelf RFID devices and can well adapt to new environments with few-shot fine-tuning, thus greatly simplifying the deployment of the trained system. We conduct extensive experiments in different indoor scenarios to validate the high adaptability and accuracy of the Meta-Pose system.