Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

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.

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

“…Accurate motion capture is essential for realistic animation, immersive virtual reality experiences, and a precise biomechanical analysis of human movements [3,4]. Traditional optical motion capture systems are widely employed, but they often exhibit certain limitations, such as high cost, restricted mobility, and dependency on controlled environments [5,6].…”

Human Motion Capture Based on MEMS Sensor

“…The human motion capture system based on the micro electro mechanical system is low cost, easy to wear, and the high precision of measuring precision, which is applied to the field of rehabilitation, virtual reality, human-computer interaction, film and television animation, military and sports training, as the development of MEMS technology [1][2]. Human motion capture schemes mainly include acoustic [3], electromagnetic [4], optical [5], inertia and so on. Acoustic, optical, electromechanical and other motion capture schemes have problems such as easy to lose signals, strict requirements on the environment and so on, and few applicable scenes.…”

Intelligent Sensors for Human Motion Analysis

“…They introduce trajectory modifications of different types and scales. In [13], four existing types of artefacts are detected, classified, and removed. The proposed algorithm is based on the derivative, low-pass filtering, mathematical morphology, loose predictor, and applicability analysis.…”