基于骨架的动作识别的等级交织图形表示学习
Xi Zhang1, Caiyan Tan2, Yuan Yuan1
1School of Arts, Sun Yat-Sen University, Guangzhou, 510275, China.
Scientific reports
|October 10, 2025
概括
本研究介绍了基于骨架的动作识别的层次交织图形学习框架 (HI-GCN). HI-GCN 增强了跨多个尺度的时空特征学习,在基准数据集上表现优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 图形卷积网络 (GCNs) 通过将骨关节作为图形建模,在基于人类骨的动作识别方面表现出色.
- 现有的GCN往往侧重于单空间上下文或整体时间序列,忽视了多尺度的时空相互作用.
- 这种限制影响了对具有不同时间动态的复杂动作的理解.
研究的目的:
- 为改进基于骨架的动作识别提出一个新的等级交织图形学习框架 (HI-GCN).
- 解决现有的GCN在捕捉多个尺度的时空关系方面的局限性.
- 通过整合不同的时间尺度来增强复杂行动的识别.
主要方法:
- 开发了分层交织图形学习框架 (HI-GCN).
- 引入了一个交织的上下文图形卷积模块,用于在各种时间尺度上的相邻中整合时空信息.
- 整合了一个移动窗口时间变压器模块,具有多尺度受体场,用于高级时间依赖模型.
主要成果:
- 在基于骨架的动作识别的多个基准数据集上,HI-GCN实现了卓越的性能.
- 在NTU RGB+D 60 (跨主题) 上获得了93.3%的准确性.
- 在NTU RGB+D 120 (跨主题) 上达到90.3%的准确率,在NW-UCLA上达到97.0%.
结论:
- 拟议的HI-GCN有效地捕捉了微妙的拓变化,并完善了时空表示.
- 该框架在基于骨架的动作识别中显示出与最先进的方法相比的显著改进.
- HI-GCN提供了一种更全面的方法,用于从骨数据中理解复杂的人类行为.
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