29 April 2016 Robust object tracking techniques for vision-based 3D motion analysis applications
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Abstract
Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system “Mosca” is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms’ evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.
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Vladimir A. Knyaz, Vladimir A. Knyaz, Sergey Yu. Zheltov, Sergey Yu. Zheltov, Boris V. Vishnyakov, Boris V. Vishnyakov, } "Robust object tracking techniques for vision-based 3D motion analysis applications", Proc. SPIE 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV, 98961D (29 April 2016); doi: 10.1117/12.2227879; https://doi.org/10.1117/12.2227879
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