基于标记器和没有标记器的坐标混合对应注册的方法,描述复杂的物体/物体相互作用
Hyeonseok Kim1, Makoto Miyakoshi1,2,3, John Rehner Iversen1,4
1Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了将2D视频与3D移动捕获数据共同注册的新方法,从而增强了运动分析. 最好的方法准确地重建手和球运动在杂,即使有遮.
科学领域:
- 生物力学 生物力学
- 计算机视觉 计算机视觉
- 运动捕捉技术的技术.
背景情况:
- 全身运动捕捉对于分析人类运动至关重要.
- 基于标记器的系统是常见的,但无标记器和混合方法正在获得引力.
- 对2D视频和3D标记数据的联合注册,特别是如果没有先前的空间知识,仍然未被充分探索.
研究的目的:
- 开发和评估2D视频与3D运动捕捉数据的联合注册方法.
- 为了将2D球坐标转换为3D空间,并在杂技等复杂活动中重建手动.
- 为应对某些身体部位或物体的遮和标记器追踪的缺失所带来的挑战.
主要方法:
- 为2D视频和3D标记数据提出了四种线性协同注册方法.
- 作为一个测试案例,利用三球级联杂作为一个测试案例,结合了手臂和手腕标记数据.
- 基于球运动约束 (保持和飞行阶段) 和引力物理学的优化方法.
主要成果:
- 尽量减少球手误差被证明是低于最佳的,扭曲了球的轨迹.
- 最优的方法利用了垂直转换的重力约束和横向转换的球握约束.
- 实现了精确的球飞行描述和手腕运动重建.
结论:
- 混合动力捕捉系统可以有效地整合2D视频和3D标记数据.
- 基于物理学的约束 (重力,球动力学) 对于准确的运动重建在联合注册中至关重要.
- 开发的方法为带有部分闭塞的复杂运动分析场景提供了可靠的解决方案.
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