计算机科学
光流
点云
计算机视觉
活动识别
人工智能
代码库
运动(物理)
移动设备
运动估计
地理标记
雷达
云计算
运动检测
点(几何)
实时计算
人机交互
电信
情报检索
图像(数学)
万维网
源代码
几何学
操作系统
数学
作者
Fangqiang Ding,Zhi-Quan Luo,Peijun Zhao,Chris Xiaoxuan Lu
出处
期刊:Cornell University - arXiv
日期:2023-06-29
标识
DOI:10.48550/arxiv.2306.17010
摘要
Approaching the era of ubiquitous computing, human motion sensing plays a crucial role in smart systems for decision making, user interaction, and personalized services. Extensive research has been conducted on human tracking, pose estimation, gesture recognition, and activity recognition, which are predominantly based on cameras in traditional methods. However, the intrusive nature of cameras 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 method for scene flow estimation as a 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 with an average 3D endpoint error of 4.6cm, significantly surpassing the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition, human parsing, and human body part tracking. To foster further research in this area, we will provide our codebase and dataset for open access upon acceptance.
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