Real-Time Estimation of Missing Markers for Reconstruction of Human Motion

Optical motion capture is a prevalent technique for capturing and analyzing movement. However, a common problem in optical motion capture is the missing marker problem due to occlusions or ambiguities. Most methods for resolving this problem either require extensive post-processing efforts or become ineffective when a significant portion of markers are missing for extended periods of time. In this paper, we present an approach to reconstruct human motion corrupted in the presence of missing or mis-tracking markers. We propose a data-driven, piecewise linear predicting kalman filter framework to estimate missing marker position, and reconstruct human motion in real time by rigid body tracking solver. It allows us to accurately and effectively reconstruct human motion within a simple extrapolation framework. We demonstrate the effectiveness of our method on real motion data captured using OptiTrack. Our experimental results demonstrate that our method is efficient in recovering human motion.

[1]  Sung Yong Shin,et al.  Computer puppetry: An importance-based approach , 2001, TOGS.

[2]  D Thalmann,et al.  Using skeleton-based tracking to increase the reliability of optical motion capture. , 2001, Human movement science.

[3]  Pascal Fua,et al.  Local and Global Skeleton Fitting Techniques for Optical Motion Capture , 1998, CAPTECH.

[4]  Jean-Christopher Nebel.  Keyframe interpolation with self-collision avoidance , 1999, Computer Animation and Simulation.

[5]  Hyeong-Seok Ko,et al.  A physically-based motion retargeting filter , 2005, TOGS.

[6]  Greg Welch,et al.  The HiBall Tracker: high-performance wide-area tracking for virtual and augmented environments , 1999, VRST '99.

[7]  Charles F. Rose,et al.  Verbs and adverbs: multidimensional motion interpolation using radial basis functions , 1999 .

[8]  Jinxiang Chai,et al.  Example-Based Human Motion Denoising , 2010, IEEE Transactions on Visualization and Computer Graphics.

[9]  M. J. Kim,et al.  Real time motion fairing with unit quaternions , 1998, Comput. Aided Des..

[10]  Joan Lasenby,et al.  A real-time sequential algorithm for human joint localization , 2005, SIGGRAPH '05.

[11]  Pascal Fua,et al.  Skeleton-based motion capture for robust reconstruction of human motion , 2000, Proceedings Computer Animation 2000.

[12]  Alberto Menache,et al.  Understanding Motion Capture for Computer Animation and Video Games , 1999 .

[13]  Guodong Liu,et al.  Estimation of missing markers in human motion capture , 2006, The Visual Computer.

[14]  Jurriaan D. Mulder,et al.  Optical Tracking and Automatic Model Estimation of Composite Interaction Devices , 2006, IEEE Virtual Reality Conference (VR 2006).

[15]  Klaus Dorfmüller-Ulhaas.  Robust Optical User Motion Tracking Using a Kalman Filter , 2005 .

[16]  Jurgen Broeren,et al.  A kinematic analysis of a haptic handheld stylus in a virtual environment: a study in healthy subjects , 2006, Journal of NeuroEngineering and Rehabilitation.

[17]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[18]  Leif Kobbelt,et al.  Self-calibrating optical motion tracking for articulated bodies , 2005, IEEE Proceedings. VR 2005. Virtual Reality, 2005..

[19]  J. Cameron,et al.  Real-Time Estimation of Missing Markers in Human Motion Capture , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[20]  Sung Yong Shin,et al.  General Construction of Time-Domain Filters for Orientation Data , 2002, IEEE Trans. Vis. Comput. Graph..

[21]  Michael F. Cohen,et al.  Verbs and Adverbs: Multidimensional Motion Interpolation , 1998, IEEE Computer Graphics and Applications.

[22]  Greg Welch,et al.  HISTORY: The Use of the Kalman Filter for Human Motion Tracking in Virtual Reality , 2009, PRESENCE: Teleoperators and Virtual Environments.