Human motion sensing plays a crucial role in smart systems for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications. To address this, mmWave radars have gained popularity due to their privacy-friendly features. In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition and human parsing and support human body part tracking.

翻译：人体运动感知在智能系统的决策制定、用户交互和个性化服务中扮演着关键角色。现有的大量研究主要基于摄像头，但其侵入性限制了其在智能家居应用中的使用。为解决此问题，毫米波雷达因其保护隐私的特性而日益受到欢迎。在本工作中，我们提出了milliFlow，一种新颖的深度学习方法，用于估计场景流作为毫米波点云的补充运动信息，其作为中间层特征，可直接惠及下游人体运动感知任务。实验结果表明，与竞争方法相比，我们的方法具有优越的性能。此外，通过融入场景流信息，我们在人体活动识别和人体解析方面取得了显著改进，并支持人体部位跟踪。