基于骨架的连续手语识别的部分智能图形里埃学习
Dong Wei1, Hongxiang Hu1, Gang-Feng Ma1
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Journal of imaging
|August 27, 2025
概括
这项研究引入了一种新的部分智能图形里埃学习方法,用于手语识别,提高准确性并降低计算成本. 这种新的方法有效地模拟了复杂的身体部位运动, 以更好地理解视觉语言.
科学领域:
- 计算机科学
- 人工智能
- 人与计算机的交互
背景情况:
- 由于计算成本和噪音干扰,手语识别面临RGB输入的挑战.
- 在手语中准确地建模非线性时间动态和身体各部位的异步是很困难的.
研究的目的:
- 为基于骨架的连续手语识别提出一个新的部分智能图形里埃学习方法 (PGF-SLR).
- 通过对身体部位的时空关系进行统一的建模来解决现有方法的局限性.
主要方法:
- 构建了一个部分水平的富里埃完全连接的图形,将身体部分视为节点,频域注意力视为边缘.
- 采用适应频率增强方法来放大歧视性作用特征.
- 使用双分支行动学习模块与辅助预测分支来增强理解.
主要成果:
- 在PHOENIX14和PHOENIX14- T数据集中分别获得了3. 31%/ 3. 70%和2. 81%/ 7. 33%的相对改善.
- 在CSL-Daily数据集上表现出竞争力,显示出强烈的概括性.
- 在线和离线设置中降低计算成本.
结论:
- 拟议的PGF-SLR方法有效地捕捉了信号语言识别频率域中的时空依赖性.
- PGF-SLR为连续的手语识别提供了一种轻量级,强大且计算效率高的解决方案.
- 这种方法对于需要准确和快速的手语理解的现实应用具有显著的潜力.
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