Abstract
Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature. The works so far mostly employ a simple noise model, expressing the uncertainty as a simple variance. In the work, we demonstrate that it might be not sufficient and we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. Such a knowledge is especially important for using optical motion capture to calibrate other techniques, and for applications requiring very fine quality of recording. For the automated readout of the noise coefficients, we solve the multidimensional regression problem using sophisticated metaheuristics in the exploration-exploitation scheme. We identified in the laboratory the notable contribution to the overall noise from white noise and random walk, and a minor contribution from blue noise and flicker, whereas the violet noise is absent. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion. We analyzed also how the noise types scale with an increasing number of cameras. We had also the opportunity to observe the influence of camera failure on the overall performance.
Highlights
Synthesis and analysis of human motion is an active research area with a plurality of applications in biomechanics and entertainment [1]
Using the single long sequence recorded with a regime as described in Sections 3.1–3.3, we have obtained a pool of sequences obtained with different camera sets
We demonstrated how to evaluate with Allan variance a compound structure of noise present in the optical Mocap system
Summary
Synthesis and analysis of human motion is an active research area with a plurality of applications in biomechanics and entertainment [1]. Technique, based on tracking of retro-reflective markers in IR images is considered the gold standard in this field of research It outperforms other techniques and it has been used for verification of the other technologies: inertial [2,3] or optical [4]. The uncertainty in optical motion capture systems depends on numerous factors, such as type and amount of used cameras, their physical setup, and mounting, marker size, environmental conditions such as air temperature or humidity, camera noise, and quality of the calibration of the motion camera in the motion capture system. This is the mean distance between the 2D image of the markers on camera and 3D reconstructions of those markers projected back to the camera’s sensor in pixels
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