NodeNS Human Activity Dataset

To date, numerous types of collection devices have been used in the recognition of human activities. However, due to the scarcity of training data, the task of 3D point cloud labelling has not yet made significant progress. To overcome this challenge, it is aimed to deduce this data requirements gap, allowing deep learning methods to reach their full potential in 3D point cloud tasks. The dataset used for this process is comprised of dense point clouds acquired with the static ground sensor by the NodeNs company supported MIMO radar (NodeNs ZERO 60 GHz IQ radar). It contains multiple types of human being data ranging from one to four individuals and encompasses a range of human action scenarios, including standing, sitting, picking up, falling, and walking.

迄今为止，已有多种类型的采集设备应用于人类活动识别领域。然而，由于训练数据稀缺，3D点云标注（3D point cloud labelling）任务尚未取得显著进展。为克服这一挑战，本研究旨在填补这一数据需求缺口，使深度学习方法能够在3D点云任务中充分发挥其潜力。本研究使用的数据集，由NodeNs公司支持的多输入多输出（Multiple-Input Multiple-Output, MIMO）雷达（NodeNs ZERO 60 GHz IQ雷达）搭载静态地面传感器采集得到的密集点云构成。该数据集包含1至4人的多类人类活动数据，涵盖站立、坐下、拾取、跌倒以及行走等多种人类动作场景。

搜集汇总

背景与挑战

背景概述

NodeNS Human Activity Dataset是一个包含多种人类活动3D点云数据的数据集，旨在解决3D点云标记任务中训练数据不足的问题。数据集通过60 GHz IQ雷达采集，涵盖站立、坐下等多种活动场景，支持深度学习方法的开发和应用。