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3D Tracking of Human Motion Using Visual Skeletonization and Stereoscopic Vision.

Matteo Zago1, Matteo Luzzago2, Tommaso Marangoni2

  • 1Department of Electronics, Information and Bioengineering, Polytechnic of Milan, Milan, Italy.

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Summary

This study validates a markerless gait analysis system using OpenPose AI and two cameras. Optimal accuracy was achieved with wider camera distance, high resolution, and straight gait, confirming feasibility for 3D motion tracking.

Keywords:
artificial intelligencecomputer visiongait analysismarkerless motion capturemovement measurement

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Area of Science:

  • Biomechanics
  • Computer Vision
  • Human Motion Analysis

Background:

  • Markerless human motion reconstruction remains a challenge.
  • AI-based landmark identification offers a low-cost alternative.
  • OpenPose enables skeleton recognition but its metrological performance needs exploration.

Purpose of the Study:

  • To validate a two-camera OpenPose markerless system for gait analysis.
  • To assess accuracy based on camera distance, gait direction, and video resolution.
  • To compare system performance against a marker-based optical motion capture system.

Main Methods:

  • Stereoscopic system calibration and synchronized video acquisition.
  • OpenPose processing for skeleton extraction and 3D joint coordinate triangulation.
  • Analysis of trajectory errors and gait parameters (step length, swing/stance time).

Main Results:

  • Lowest trajectory error (~20 mm) with 1.8m camera distance, high resolution, and straight gait.
  • System underestimated step length by ~1.5 cm; no systematic bias in swing/stance time.
  • Step length accuracy varied significantly with gait direction, camera distance, and resolution.

Conclusions:

  • The OpenPose-based system is feasible for 3D gait analysis using low-cost hardware.
  • Maximizing camera distance and video resolution enhances metrological performance.
  • This approach enables unobtrusive tracking of kinematics and gait parameters.