重复的跨尺度结构诱导的特征融合网络用于2D手姿势估计
Xin Guan1, Huan Shen1, Charles Okanda Nyatega2
1School of Microelectronics, Tianjin University, Tianjin 300072, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一种用于手姿势估计的新型网络,提高了自闭关键点的准确性. 该方法利用跨尺度的特征融合来更好地了解手的结构,并找到难以看到的关节.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 从RGB图像中估计手姿势已经通过卷积神经网络取得了进展.
- 推断自我封闭的关键点仍然是手势估计的一个重大挑战.
- 传统的外观特征不足以识别封闭的关键点,需要上下文信息.
研究的目的:
- 为了应对自封闭关键点推断的挑战,在手上设置估计.
- 提出一种新的网络架构,利用上下文信息增强功能学习.
- 为了提高二维手姿势估计的准确性,特别是对于封闭的关键点.
主要方法:
- 建议建立一个重复的跨尺度结构诱导的特征融合网络.
- 该网络包括两个模块:全球网络用于初始联合本地化,区域网络用于改进.
- 全球网采用特征金字塔结构,结合语义和全球空间信息.
- 区域网络采用四个阶段的跨度特征融合来学习外观特征,由手结构来告知.
主要成果:
- 与最先进的技术相比,拟议的方法显示出更高的性能.
- 在STB和RHD公共数据集上进行评估,用于2D手姿势估计.
- 在定位关键点方面获得了更高的准确性,特别是在自我封闭条件下.
结论:
- 新型网络通过整合多层次特征和上下文关系,有效地学习关键点表示.
- 拟议的方法显著提高了推断手部姿势估计中自我排除关键点的能力.
- 该方法在2D手姿势估计准确性和稳定性方面取得了重大进展.
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