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3D Visualization of Body Motion in Speed Climbing

机译:速度攀岩的身体运动的3D可视化

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Speed climbing involves an optimization of the velocity of the ascent and the trajectory path during performance. Consequently, any amount of energy spent in the two other directions than vertical, namely the lateral direction and the direction perpendicular to the wall plane, is a potential loss of performance. To assess this principle, we present a study on 3D motion analysis and its 3D visualization for a subject during a speed climbing performance. The fundamentals of geometrical measurement in 3D require to integrate multiple 2D cues, at least two, in order to extract 3D information. First results with two drones following an athlete's ascent show that a 3D velocity profile can be provided from the tracking of a marker on the harness, pointing critical phases in the ascent where the vertical speed is not dominant any more. We further investigate 3D motion of full body using markerless video-based tracking. Our approach is based on a full body 3D avatar model of the climber, represented as a 3D mesh. This model and its deformation are learned in a laboratory studio. The learning needs to be done only once. Result is a manifold embedding of the 3D mesh and its deformations, which can be used afterwards to perform registration onto video of performance of speed climbing. The results of the tracking is an inference of the 3D mesh aligned onto videos of speed climbing performance. From this 3D mesh, we deduce an estimation of the center of mass (COM). We show that this estimation from 3D mesh differs from the usual approximation of the COM as a marker on the harness. In particular, the 3D mesh COM takes into account the whole body movement such as the influence of the limbs which is not detected by a marker on the harness.
机译:速度攀登涉及优化性能期间上升的速度和轨迹路径。因此,在两个其他方向上花费的能量比垂直,即横向方向和垂直于壁平面的方向,是潜在的性能损失。为了评估这一原则,我们在速度攀爬性能期间对3D运动分析及其3D可视化进行了研究。 3D中的几何测量基础知识要求将多个2D线索(至少两个)集成,以提取3D信息。首先是运动员上升后两个无人机的结果表明,可以从线束上的跟踪提供3D速度分布,指向垂直速度不得占主导地位的上升中的关键阶段。我们进一步使用无标记基于视频跟踪来研究全身的3D运动。我们的方法基于登山者的全身3D化身模型,表示为3D网格。该模型及其变形在实验室工作室学习。学习需要只做一次。结果是3D网格及其变形的歧管嵌入,之后可以使用,以便对速度攀爬性能的视频进行登记。跟踪结果是3D网格的推断,对准升空性能的视频。从这个3D网格,我们推断出质量中心(COM)的估计。我们表明,来自3D网格的此估计与COM作为线束上的标记的通常近似。特别地,3D网格COM考虑了整个身体运动,例如由线束上未检测到的肢体的影响。

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