Human Performance Research Graz, University of Graz and Medical University of Graz, Max-Mell-Allee 11, 8010 Graz, Austria.
J Biomech. 2011 Jul 28;44(11):2172-6. doi: 10.1016/j.jbiomech.2011.05.019. Epub 2011 Jun 2.
Recording and reconstruction of 3D motion capturing data relies on fixed, static camera positions with given inter-camera distances in a laboratory frame. To overcome this limitation, we present a correction algorithm that allows us to address camera movements in moving camera setups. Camera vibrations are identified by comparison of specialized target positions in dynamic measurements with their respective positions in static trials. This results in a 2D shift vector Δw with which the individual camera streams are corrected. The capabilities of this vibration reduction procedure are demonstrated in a test setup of four cameras that are (i) separately and (ii) simultaneously perturbed while capturing a static test object. In the former case, the correction algorithm is capable of reducing the reconstruction residuals to the order of the calibrations residual and enables reconstruction in the latter case, which is impossible without any correction. This approach extends the application of marker-based infrared motion tracking to moving and even accelerated camera setups.
3D 运动捕捉数据的记录和重建依赖于实验室框架中具有给定相机间距离的固定、静态相机位置。为了克服这一限制，我们提出了一种校正算法，允许我们在移动相机设置中处理相机运动。通过将动态测量中专门的目标位置与其在静态试验中的相应位置进行比较，可以识别相机振动。这会产生一个 2D 位移矢量 Δw，通过该矢量可以校正各个相机流。该振动减小过程的功能在一个由四个相机组成的测试设置中得到了验证，这些相机（i）分别和（ii）同时在捕获静态测试对象时受到干扰。在前一种情况下，校正算法能够将重建残差减小到校准残差的量级，并在后一种情况下实现重建，而如果不进行任何校正，则无法实现重建。这种方法将基于标记的红外运动跟踪的应用扩展到了移动甚至加速的相机设置。
