Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

Version 1 : Received: 15 March 2022 / Approved: 17 March 2022 / Online: 17 March 2022 (09:32:22 CET)

A peer-reviewed article of this Preprint also exists.

Skurowski, P.; Pawlyta, M. Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences. Sensors 2022, 22, 4076. Skurowski, P.; Pawlyta, M. Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences. Sensors 2022, 22, 4076.

Journal reference: Sensors 2022, 22, 4076
DOI: 10.3390/s22114076

Abstract

Optical motion capture systems are prone to the errors connected with markers recognition – occlusion, leaving the scene or mislabelling – all these errors are then corrected in the software, but still, the process is not perfect, resulting in artifact distortions. In the article, we examine four existing types of artifacts, then propose the 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, comparison of performance to the human operators on real life data and applicability analysis for the distortion removal.

Keywords

motion capture; artifact classification; artifact detection; reconstruction

Subject

MATHEMATICS & COMPUTER SCIENCE, Other

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