Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

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.

Automated summary

This article examines the distortion issues in optical motion capture systems and proposes a method for detecting and classifying these distortions. The algorithm employs derivative analysis, low-pass filtering, mathematical morphology, and loose predictor. Tests involving simulated distorted sequences, performance comparisons with human operators, and an analysis of distortion removal applicability were conducted.