基于MediaPipe可注册系统的实时手势监控模型
Yuting Meng1, Haibo Jiang2, Nengquan Duan1
1College of Mechanical Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的可注册的手势识别系统,使用三重损失. 它通过学习手势差异并允许新的手势注册来实现个性化的手势识别.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 手势识别对于互动至关重要,但目前的方法缺乏灵活性.
- 现有的系统在与现实世界手势的多样性和变化作斗争.
研究的目的:
- 提出一种新的可注册手势识别方法.
- 通过允许用户注册新的手势来实现个性化的手势识别.
主要方法:
- 开发了一个可注册的手势数据集 (RGDS).
- 提出了基于三重损失的方法来学习手势差异和集群.
- 引入了一个规范化技术和一个FingerComb块用于功能增强.
- 改进了ResNet和拟议的FingerNet,用于可注册的单手手势识别.
主要成果:
- 拟议的FingerNet模型在RGDS数据集上表现出强的表现.
- 该系统成功实现了可记录的手势识别.
- 该方法有效地聚合并识别新添加的手势.
结论:
- 开发的系统为手势识别提供了灵活和个性化的解决方案.
- 这种方法解决了固定手势识别系统的局限性.
- 可记录性质允许适应性和用户特定的手势交互.
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