论文标题
MRI：使用MMWave，RGB-D和惯性传感器的多模式3D人姿势估计数据集
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors
论文作者
论文摘要
估计3D人体姿势和运动的能力，也称为人体姿势估计（HPE），可以为家庭健康监测（例如远程康复训练）提供许多应用。使用RGB摄像机，深度传感器，毫米波（MMWAVE）雷达和可穿戴惯性传感器的传感器出现了几种可能的解决方案。尽管以前在HPE的数据集和基准测试方面进行了努力，但很少有数据集利用多种方式，并专注于基于家庭的健康监测。为了弥合差距，我们提出了MRI，这是一种具有MMWAVE，RGB-D和惯性传感器的多模式3D人姿势估计数据集。我们的数据集由20个受试者进行康复练习的160k超过160K同步框架组成，并支持HPE和动作检测的基准。我们使用数据集进行了广泛的实验，并描绘了每种模式的强度。我们希望MRI的发布可以通过姿势估计，多模式学习和动作理解来催化研究，更重要的是促进了基于家庭健康监测的应用。
The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge the gap, we present mRI, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 160k synchronized frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality. We hope that the release of mRI can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly facilitate the applications of home-based health monitoring.
