HGR-ViT:用视觉变压器识别手势
Chun Keat Tan1, Kian Ming Lim1, Roy Kwang Yang Chang1
1Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了HGR-ViT,这是用于高级手势识别 (HGR) 的视觉转换器模型. 这种新方法显著提高了在各种数据集中识别手势的准确性,增强了人机交互.
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 (HGR) 对沟通和人机交互至关重要.
- 现有的深度学习模型往往忽略了关键的手指方向和位置信息.
- 需要强大的HGR模型来捕捉空间细节.
研究的目的:
- 提出HGR-ViT,一个新的视觉转换器 (ViT) 模型,用于准确的手势识别.
- 为了解决先前的HGR方法中编码手的位置和方向的局限性.
- 通过基于注意力的机制来提高HGR系统的性能.
主要方法:
- 拟议的HGR-ViT模型使用视觉变压器架构.
- 手势图像被分为固定大小的补丁,并添加了位置嵌入.
- 一个标准的变压器编码器处理这些嵌入,然后由一个多层感知子进行分类.
主要成果:
- 在ASL数据集上,HGR-ViT实现了非常高的准确性:99.98%.
- 该模型在数字 (99.36%) 和NUS数据集 (99.85%) 的ASL上显示出高性能.
- 包括位置嵌入式有效地捕获空间手信息.
结论:
- HGR-ViT显著提高了手势识别的准确性.
- 模型编码位置信息的能力是其卓越性能的关键.
- 这种方法为改善人机交互和手语识别提供了一个有希望的方向.
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