ArticleSensors (Basel, Switzerland)2022
Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Semi-automated object-based image analysis for peri-urban land use/cover classification using WorldView-3 imagery of Mankweng, South Africa.Scientific reports · 2026Article
- Dual Foot-Mounted Localisation Scheme Employing a Minimum-Distance-Constraint Kalman Filter Under Coloured Measurement Noise.Micromachines · 2024Article
- Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture.Micromachines · 2023Article
- Intelligent Sensors for Human Motion Analysis.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors.
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Abstract
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.
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