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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Relative Motion Analysis using Rotating Axes01:25

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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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Related Experiment Video

Updated: Jun 1, 2026

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

An algorithm to correct for camera vibrations in optical motion tracking systems.

P Huber1, C Cagran, W Müller

  • 1Human Performance Research Graz, University of Graz and Medical University of Graz, Max-Mell-Allee 11, 8010 Graz, Austria. philipp.huber@uni-graz.at

Journal of Biomechanics
|June 7, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a correction algorithm for 3D motion capture, enabling accurate data recording even with moving cameras. The method corrects camera vibrations, extending motion tracking applications to dynamic environments.

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Last Updated: Jun 1, 2026

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Area of Science:

  • Biomechanics
  • Computer Vision
  • Robotics

Background:

  • Marker-based infrared motion tracking typically requires static camera setups.
  • Camera movement introduces significant errors in 3D motion reconstruction.
  • Existing methods are limited in handling dynamic or accelerated camera systems.

Purpose of the Study:

  • To develop and validate a correction algorithm for 3D motion capture data affected by camera vibrations.
  • To enable accurate motion reconstruction in setups with moving or accelerated cameras.
  • To extend the applicability of marker-based motion tracking to non-laboratory environments.

Main Methods:

  • A correction algorithm was developed to identify camera vibrations using specialized target positions.
  • 2D shift vectors (Δw) were calculated by comparing dynamic and static target positions.
  • Individual camera streams were corrected using the derived shift vectors.
  • The algorithm was tested on a four-camera system with separate and simultaneous perturbations.

Main Results:

  • The correction algorithm significantly reduced reconstruction residuals to the level of calibration residuals in cases of individual camera perturbation.
  • Accurate 3D motion reconstruction was achieved even when multiple cameras were simultaneously perturbed, a feat impossible without correction.
  • The method demonstrated robustness in reducing vibration effects on motion capture data.

Conclusions:

  • The proposed correction algorithm effectively addresses camera vibrations in 3D motion capture systems.
  • This technique expands the utility of marker-based infrared motion tracking to dynamic and accelerated camera setups.
  • The approach facilitates more versatile and real-world applications of motion capture technology.