Effects of camera switching on fine accuracy in a motion capture system.

When using optical motion capture systems, increasing the number of cameras improves the visibility. However, the software used to deal with the information fusion from multiple cameras may compromise the accuracy of the system due to camera dropout, which can vary with time. In cadaver studies of radial head motion, increasing the number of cameras used by the motion capture system seemed to decrease the accuracy of the measurements. This study investigates the cause. The hypothesis was that errors in position can be induced when markers are obscured from and then restored to a camera's viewable range, as can happen in biomechanical studies. Accuracy studies quantified the capabilities of the motion capture system with precision translation and rotation movements. To illustrate the effect that abrupt perceived changes in a marker's position can have on the calculation of radial head travel, simulated motion experiments were performed. In these studies, random noise was added to simulated data, which obscured the resultant path of motion. Finally, camera-blocking experiments were performed in which precise movements were measured with a six-camera Vicon system and the errors between the actual and perceived motion were computed. During measurement, cameras were selectively blocked and restored to view. The maximum errors in translation and rotation were 3.7 mm and 0.837 deg, respectively. Repeated measures analysis of variance (ANOVAs) (alpha=0.05) confirmed that the camera-blocking influenced the results. Taken together, these results indicate that camera-switching can affect the observation of fine movements using a motion analysis system with a large number of cameras. One solution is to offer opportunity for user interaction in the software to choose the cameras used for each instant of time.

[1]  Michael Gleicher,et al.  Evaluating video-based motion capture , 2002, Proceedings of Computer Animation 2002 (CA 2002).

[2]  M. Cifrek,et al.  Camera parameter initialization for 3D kinematic systems , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[3]  S Miyazaki,et al.  Comparison of the performance of 30 camera systems , 1995 .

[4]  Lorin P Maletsky,et al.  Simulating dynamic activities using a five-axis knee simulator. , 2005, Journal of biomechanical engineering.

[5]  Qingshan Chen,et al.  Technical note: validation of a motion analysis system for measuring the relative motion of the intermediate component of a tripolar total hip arthroplasty prosthesis. , 2005, Medical engineering & physics.

[6]  Gideon P. Stein Accurate internal camera calibration using rotation, with analysis of sources of error , 1995, Proceedings of IEEE International Conference on Computer Vision.

[7]  Peter I. Corke,et al.  The Effect of Noise on Camera Calibration Parameters , 2001, Graph. Model..

[8]  T. M. Kepple,et al.  The presentation and evaluation of a video based, six degree-of-freedom approach for analyzing human motion , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Shinji Miyazaki,et al.  Comparison of the performance of 3D camera systems , 1995 .

[10]  Michael Gleicher Comparative Analysis of Constraint-Based Motion Editing Methods , 2001 .

[11]  A. Cappozzo,et al.  Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. , 2004, Gait & posture.

[12]  L. Maletsky,et al.  Accuracy of an optical active-marker system to track the relative motion of rigid bodies. , 2007, Journal of biomechanics.

[13]  Stefano Soatto,et al.  A Variational Approach to Problems in Calibration of Multiple Cameras , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  U.D. Croce,et al.  Surface-marker cluster design criteria for 3-D bone movement reconstruction , 1997, IEEE Transactions on Biomedical Engineering.

[15]  Z.O. Abu-Faraj,et al.  A clinical system for the analysis of three-dimensional pediatric foot and ankle motion , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[16]  Adrien Bartoli,et al.  Euclidean reconstruction independent on camera intrinsic parameters , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[17]  A. M. Tekalp,et al.  Multiple camera tracking of interacting and occluded human motion , 2001, Proc. IEEE.

[18]  R. Mohr,et al.  What accuracy for 3D measurements with cameras? , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[19]  Jake K. Aggarwal,et al.  Automatic tracking of human motion in indoor scenes across multiple synchronized video streams , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[20]  Chris Miller,et al.  A quasi-static method for determining the characteristics of a motion capture camera system in a "split-volume" configuration. , 2002, Gait & posture.

[21]  Joachim Weickert,et al.  Median and related local filters for tensor-valued images , 2007, Signal Process..

[22]  A Leardini,et al.  Position and orientation in space of bones during movement: experimental artefacts. , 1996, Clinical biomechanics.

[23]  Katsu Yamane,et al.  High-precision and high-speed motion capture combining heterogeneous cameras , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[24]  G. Sampath,et al.  Design and development of an active marker based system for analysis of 3-D pediatric foot and ankle motion , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[25]  Mark Carl Miller,et al.  The effect of the annular ligament on kinematics of the radial head. , 2007, The Journal of hand surgery.

[26]  J. Richards,et al.  The measurement of human motion: A comparison of commercially available systems , 1999 .

[27]  Jake K. Aggarwal,et al.  Tracking Human Motion in Structured Environments Using a Distributed-Camera System , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Rudolf Mester,et al.  The Role of Total Least Squares in Motion Analysis , 1998, ECCV.

[29]  Ricardo M. L. Barros,et al.  A flexible software for tracking of markers used in human motion analysis , 2003, Comput. Methods Programs Biomed..

[30]  N. Alberto Borghese,et al.  Tracking densely moving markers , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[31]  A. G. Amitha Perera,et al.  A unified framework for tracking through occlusions and across sensor gaps , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  S. Evans,et al.  Accuracy and repeatability of an optical motion analysis system for measuring small deformations of biological tissues. , 2007, Journal of biomechanics.