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). 2022 May 27;22(11):4076. doi: 10.3390/s22114076.
Optical motion capture systems are prone to errors connected to marker recognition (e.g., occlusion, leaving the scene, or mislabeling). These errors are then corrected in the software, but the process is not perfect, resulting in artifact distortions. In this article, we examine four existing types of artifacts and propose a method for detection and classification of the distortions. The algorithm is based on the derivative analysis, low-pass filtering, mathematical morphology, and loose predictor. The tests involved multiple simulations using synthetically-distorted sequences, performance comparisons to human operators (concerning real life data), and an applicability analysis for the distortion removal.
光学运动捕捉系统容易出现与标记识别相关的错误（例如遮挡、离开场景或标记错误）。这些错误会在软件中进行修正，但这个过程并不完美，会导致伪影失真。在本文中，我们研究了四种现有的伪影类型，并提出了一种用于检测和分类失真的方法。该算法基于导数分析、低通滤波、数学形态学和宽松预测器。测试涉及使用合成失真序列进行多次模拟、与人类操作员（涉及实际数据）的性能比较，以及对失真去除的适用性分析。
