Millimeter wave (mmWave) radar is a non-intrusive privacy and relatively convenient and inexpensive device, which has been demonstrated to be applicable in place of RGB cameras in human indoor pose estimation tasks. However, mmWave radar relies on the collection of reflected signals from the target, and the radar signals containing information is difficult to be fully applied. This has been a long-standing hindrance to the improvement of pose estimation accuracy. To address this major challenge, this paper introduces a probability map guided multi-format feature fusion model, ProbRadarM3F. This is a novel radar feature extraction framework using a traditional FFT method in parallel with a probability map based positional encoding method. ProbRadarM3F fuses the traditional heatmap features and the positional features, then effectively achieves the estimation of 14 keypoints of the human body. Experimental evaluation on the HuPR dataset proves the effectiveness of the model proposed in this paper, outperforming other methods experimented on this dataset with an AP of 69.9 %. The emphasis of our study is focusing on the position information that is not exploited before in radar singal. This provides direction to investigate other potential non-redundant information from mmWave rader.

翻译：毫米波雷达是一种非侵入式、保护隐私且相对便捷廉价的设备，已被证明可在人体室内姿态估计任务中替代RGB相机。然而，毫米波雷达依赖于收集来自目标的反射信号，且包含信息的雷达信号难以被充分利用。这一直是阻碍姿态估计精度提升的长期难题。为应对这一主要挑战，本文提出了一种概率图引导的多格式特征融合模型ProbRadarM3F。这是一个新颖的雷达特征提取框架，它并行使用传统的FFT方法和基于概率图的位置编码方法。ProbRadarM3F融合了传统的热图特征与位置特征，从而有效实现了人体14个关键点的估计。在HuPR数据集上的实验评估证明了本文所提模型的有效性，其以69.9%的平均精度优于在该数据集上实验的其他方法。我们研究的重点在于挖掘雷达信号中此前未被利用的位置信息。这为探索毫米波雷达中其他潜在的非冗余信息提供了方向。