合并和分割图形卷积网络用于基于骨架的交互识别
Haoqiang Wang1, Yong Wang1, Sheng Yan1
1School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
Cyborg and bionic systems (Washington, D.C.)
|March 21, 2024
概括
本研究介绍了用于人类互动识别的合并与分割图形卷积网络 (MS-GCN). 新的MS-GCN有效地捕捉了身体部位的相关性,在基准数据集上取得了最先进的结果.
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:
- 传统的图形卷积网络经常忽略在相互作用识别中身体部位之间的关键相关性特征.
- 捕捉这些身体间的部分依赖关系对于准确理解人类互动至关重要.
研究的目的:
- 为人类互动识别开发一种创新方法,有效地捕捉不同身体部位之间的相关性特征.
- 通过将互动识别视为全球性问题来解决现有方法的局限性.
主要方法:
- 介绍了采用新的合并与分割图形结构的合并与分割图形卷积网络 (MS-GCN).
- 开发了一个合并和分割图形卷积模块,通过将图形结构与图形卷积网络相结合来提取交互特征.
- 纳入了一个关节和运动特征的短期依赖模块和一个层次导向注意模块,用于层次集之间的相关性.
主要成果:
- 在NTU60和NTU120的人际交互识别数据集上实现了最先进的性能.
- 证明了该模型在捕捉身体各部分相互作用之间的复杂相关性特征方面的有效性.
- 经过广泛的实验验证,证实了模型的稳定性和有效性.
结论:
- 拟议的MS-GCN模型通过有效捕捉身体部位相关性,显著提升了人类互动识别.
- 这些新的架构组件能够提取丰富的语义和时间信息,这些信息对于互动理解至关重要.
- 开源代码促进了该领域的进一步研究和开发.
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