Department of Graphics, Computer Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland.
Sensors (Basel). 2021 Sep 12;21(18):6115. doi: 10.3390/s21186115.
Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied to the gap-filling problem in motion capture sequences within the FBM framework providing a representation of body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out that, for longer sequences, simple linear feedforward neural networks can outperform the other, sophisticated architectures, but these outcomes might be affected by the small amount of data availabe for training. We were also able to identify that the acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.
光学运动捕捉是一种成熟的现代技术，用于获取运动数据；然而，它并非无错误的。由于技术限制和标记物的遮挡，此类记录中可能会出现间隙。本文回顾了在 FBM 框架内应用于运动捕捉序列中的间隙填补问题的各种神经网络架构，为身体运动结构提供了表示。结果与插值和矩阵完成方法进行了比较。我们发现，对于较长的序列，简单的线性前馈神经网络可以优于其他复杂的架构，但这些结果可能会受到训练数据量少的影响。我们还能够确定输入序列的加速度和单调性是对获得的结果有显著影响的参数。
