MuTr:多级变压器用于从全场景深度图像中对手姿势进行估计.
Jakub Kanis1, Ivan Gruber1, Zdeněk Krňoul1
1Department of Cybernetics and New Technologies for the Information Society, University of West Bohemia Technická 8, 301 00 Pilsen, Czech Republic.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了DePOTR,这是一种基于变压器的手姿势估计方法,实现了最先进的结果. 一种新的多阶段方法,MuTr,通过全场景深度图像进一步提高了性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确的手姿势估计对于人机交互和机器人技术至关重要.
- 现有的方法通常需要单独的模型来定位手和估计姿势.
研究的目的:
- 介绍DePOTR,一种基于变压器的新方法用于手部姿势估计.
- 为了开发MuTr,从全场景深度图像对手姿势进行了多阶段的估计.
- 为标准和全场景图像设置展示统一的模型架构.
主要方法:
- 德波特:一种基于变压器的架构,用于直接估计手的姿势.
- MuTr:一个多阶段的方法,集成手部定位和来自深度图像的姿势估计.
- 评估包括纽约大学在内的四个基准数据集.
主要成果:
- 德波特超越了现有的基于变压器的方法,并与最先进的性能相匹配.
- 在没有单独的本地化和估计模型的情况下,MuTr实现了具有竞争力的结果.
- 在纽约大学的数据集中,DePOTR和MuTr分别达到7.85mm和8.71mm的精度.
结论:
- 拟议的DePOTR和MuTr方法代表了手势估计的重大进步.
- 一个单一的模型架构可以有效地处理标准和全场景深度图像场景.
- 这些方法为手姿势估计应用提供了更简单,更有效的管道.
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