基于对人体关节检测精度深入分析的姿势估计的新方法
1Department of EC Engineering, CSPIT, CHARUSAT, Anand, India.
PeerJ. Computer science
|June 22, 2023
概括
这项研究使用BlazePose模型从图像中准确估计了姿势,在检测像女神这样的姿势的关键身体关节方面实现了高准确度.
科学领域:
- 计算机视觉 计算机视觉
- 生物机械分析 生物机械分析
- 机器学习用于体育运动
背景情况:
- 从图像中估计人类姿势对于体育分析等应用至关重要.
- 由于的复杂性和多样性,提出了独特的挑战.
- 准确的姿势估计可以帮助实践和绩效评估.
研究的目的:
- 开发和评估一种基于神经网络的方法,用于从RGB图像中估计姿势.
- 评估BlazePose模型在检测特定身体关键点方面的准确性.
- 使用正确关键点百分比 (PCK) 和检测到关节百分比 (PDJ) 来量化姿势估计性能.
主要方法:
- 利用BlazePose模型从五种姿势的图像中检测到多达33个身体关键点.
- 手动注释地面真相关键点,使用Makesense AI进行准确性比较.
- 开发了一个算法来计算PCK和PDJ,基于预测和真实关键点之间的距离.
主要成果:
- BlazePose模型在定位姿势的身体关节方面表现出很高的准确性.
- 在女神姿势中获得了93.9%的PCK,表明了出色的整体姿势估计.
- 大多数身体关节在惊人的姿势中PDJ值在90%到100%之间.
结论:
- BlazePose模型是有效的从图像准确的姿势估计.
- 开发的指标 (PCK和PDJ) 提供了对姿势检测准确性的可靠评估.
- 这种方法在自动化分析和反系统中具有潜在的应用.
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