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在3D人类骨架上深度学习以识别人类活动:调查和比较研究

Hung-Cuong Nguyen¹, Thi-Hao Nguyen¹, Rafał Scherer²

¹Faculty of Engineering Technology, Hung Vuong University, Viet Tri City 35100, Vietnam.

Sensors (Basel, Switzerland)

|June 10, 2023

概括

本调查回顾了使用3D骨架数据进行人类活动识别 (HAR) 的深度学习. 它涵盖了从2019年到2023年的循环神经网络,卷积神经网络,图形卷积网络和混合深度神经网络.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

人类活动识别 (HAR) 对于人机交互和监控至关重要.
基于的HAR提供了直观而有效的应用程序.
对当前的深度学习方法的全面理解对于产品开发至关重要.

研究的目的:

对基于3D骨架的HAR进行深度学习方法的全面调查.
分析和比较不同的深度学习架构,包括RNN,CNN,GCN和混合DNN.
为2019年至2023年3月的模型,数据集,指标和结果提供最新的概述.

主要方法:

对基于3D骨架的HAR深度学习模型的系统文献综述.
基于网络类型的方法分类:RNN,CNN,GCN和混合DNN.
在KLHA3D 102和KLYOGA3D数据集上对选定的深度学习网络进行比较分析.

主要成果:

详细介绍了2019-2023年的HAR模型,数据集和绩效指标.
对比研究强调了CNN,GCN和混合DNN方法的性能.
对基于3D骨架的HAR的不同深度学习架构的优缺点进行分析.

结论:

深度学习,特别是GCN和混合DNN,对基于3D骨架的HAR显著有前途.
该调查为选择 HAR 解决方案的研究人员和开发人员提供了宝贵的见解.
进一步的研究可以专注于改进混合模型和探索新的特征提取技术.

关键词:

3D人类姿势/骨架在 KLHA3D 102 数据集.在 KLYoga3D 数据集中.卷积神经网络 (CNN) 是一种神经网络.深度神经网络是一个神经网络.图形卷积网络 (GCN) 是一个图形卷积网络.人类活动的认可人类活动的认可经常性神经网络 (RNN)

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