动态的日语手语识别 抛出 手 Pose 估计 使用有效的特征提取和分类方法
Manato Kakizaki1, Abu Saleh Musa Miah1, Koki Hirooka1
1School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了一种新的动态日本手语 (JSL) 识别系统. 它在识别JSL手势方面取得了很高的准确性,改善了聋人和听力障碍者社区的沟通.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 日本手语 (JSL) 对日本的聋人社区至关重要.
- 在之前的认可研究中,由于JSL的复杂性,JSL的动态方面基本上被排除在外.
- 现有的动态JSL识别系统表现不满意.
研究的目的:
- 开发一个准确的动态日本手语识别系统.
- 克服与JSL复杂性和动态性质相关的挑战.
- 提高聋人和听障人口的通信可访问性.
主要方法:
- 在RGB摄像头录像中使用了通过MediaPipe的手姿势估计.
- 提出了适用于静态和动态手势的四种新型特征类型.
- 采用随机森林 (RF) 进行特征选择和支持矢量机 (SVM) 进行分类.
主要成果:
- 在JSL专有数据集上实现了97.20%的识别准确度.
- 在LSA64动态数据集上实现了98.40%的识别准确度.
- 证明了拟议的特征提取和选择方法的有效性.
结论:
- 开发的系统有效地识别动态JSL手势,解决先前的局限性.
- 这种方法具有很大的潜力,可以弥合JSL用户的通信差距.
- 该方法对全球手语识别进步具有更广泛的意义.
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