Millimetre 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.

通过概率地图引导的多格式特征融合模型 ProbRadarM3F，本文提出了一种用于人体姿态估计的新型毫米波雷达特征提取框架，有效地实现了人体的 14 个关键点的估计。实验证明了该模型的有效性，其 AP 达到 69.9%，突出了研究中未被利用的位置信息，为探索毫米波雷达中的其他潜在非冗余信息提供了方向。