基于姿势的静态手语识别与深度学习为土耳其语,阿拉伯语和美国手语
Rıdvan Yayla1, Hakan Üçgün1, Mahmud Abbas1
1Bilecik Şeyh Edebali University, Faculty of Engineering, Department of Computer Engineering, Bilecik 11100, Türkiye.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了土耳其语,美国英语和阿拉伯语的跨语言手语识别 (SLR) 框架. 视觉变压器和状态空间模型在识别各种手语方面表现出卓越的表现.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 计算语言学 计算语言学
背景情况:
- 人工智能的进步改善了听力障碍者的沟通.
- 跨语言手语识别 (SLR) 系统对于弥合沟通差距至关重要.
- 现有的SLR框架往往缺乏跨多种语言的稳定性.
研究的目的:
- 为土耳其语,美国英语和阿拉伯语提供一个强大的跨语言手语识别 (SLR) 框架.
- 为了比较不同深度学习架构对SLR的有效性.
- 为基于姿势的SLR系统提供对模型概括的见解.
主要方法:
- 利用MediaPipe库进行高效的手动地标提取.
- 从九个公共领域来源构建了土耳其语,美国英语和阿拉伯手语的各种数据集.
- 进行了对ConvNeXt (基于CNN),Swin Transformer (基于ViT) 和Vision Mamba (基于SSM) 架构的比较评估.
主要成果:
- 视觉变压器和状态空间模型在捕捉空间线索方面表现出卓越的性能.
- 该框架实现了跨语言的稳定和一致的特征表示.
- 对比分析强调了跨不同手语的模型概括能力.
结论:
- 当代视觉变压器和状态空间模型对于跨语言的单反相机非常有效.
- 该研究为选择基于姿势的SLR系统中合适的模型提供了有价值的见解.
- 开发的框架提高了听力障碍患者的沟通可访问性.
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