论文标题
使用mmwave Point Cloud的快速，可扩展的人姿势估算
Fast and Scalable Human Pose Estimation using mmWave Point Cloud
论文作者
论文摘要
毫米波（mmwave）雷达可以实现具有低成本和计算要求的高分辨率人姿势估计。但是，MMWave Data Point Cloud是处理算法的主要输入，比其他替代方案（例如视频帧）高度稀疏，并且信息的信息较少。此外，标有稀缺的MMWave数据阻碍了机器学习（ML）模型的开发，这些模型可以推广到看不见的情况。我们提出了一个快速，可扩展的人类姿势估计（FUSE）框架，该框架结合了多框表示和元学习，以应对这些挑战。实验评估表明，保险丝适应了看不见的场景4 $ \ times $的速度比当前的监督学习方法快，并估计了大约7 cm平均绝对错误的人类关节坐标。
Millimeter-Wave (mmWave) radar can enable high-resolution human pose estimation with low cost and computational requirements. However, mmWave data point cloud, the primary input to processing algorithms, is highly sparse and carries significantly less information than other alternatives such as video frames. Furthermore, the scarce labeled mmWave data impedes the development of machine learning (ML) models that can generalize to unseen scenarios. We propose a fast and scalable human pose estimation (FUSE) framework that combines multi-frame representation and meta-learning to address these challenges. Experimental evaluations show that FUSE adapts to the unseen scenarios 4$\times$ faster than current supervised learning approaches and estimates human joint coordinates with about 7 cm mean absolute error.
