Efficient and Robust Shape Matching for Model Based Human Motion Capture

In this paper we present a robust and efficient shape matching approach for Marker-less Motion Capture. Extracted features such as contour, gradient orientations and the turning function of the shape are embedded in a 1-D string. We formulate shape matching as a Linear Assignment Problem and propose to use Dynamic Time Warping on the string representation of shapes to discard unlikely correspondences and thereby to reduce ambiguities and spurious local minima. Furthermore, the proposed cost matrix pruning results in robustness to scaling, rotation and topological changes and allows to greatly reduce the computational cost. We show that our approach can track fast human motions where standard articulated Iterative Closest Point algorithms fail.

[1]  Hans-Peter Seidel,et al.  Scaled Motion Dynamics for Markerless Motion Capture , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  A. Volgenant,et al.  A shortest augmenting path algorithm for dense and sparse linear assignment problems , 1987, Computing.

[3]  Ian D. Reid,et al.  Articulated Body Motion Capture by Stochastic Search , 2005, International Journal of Computer Vision.

[4]  Raquel Urtasun,et al.  Combining discriminative and generative methods for 3D deformable surface and articulated pose reconstruction , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Michael J. Black,et al.  Combined discriminative and generative articulated pose and non-rigid shape estimation , 2007, NIPS.

[6]  Xu Zhao,et al.  Generative Estimation of 3D Human Pose Using Shape Contexts Matching , 2007, ACCV.

[7]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[8]  Remco C. Veltkamp,et al.  State of the Art in Shape Matching , 2001, Principles of Visual Information Retrieval.

[9]  Michael Hofmann,et al.  Multi-view 3D human pose estimation combining single-frame recovery, temporal integration and model adaptation , 2009, CVPR.

[10]  Jitendra Malik,et al.  Recovering 3D human body configurations using shape contexts , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Ahmed M. Elgammal,et al.  Coupled Visual and Kinematic Manifold Models for Tracking , 2010, International Journal of Computer Vision.

[12]  Michael J. Fischer,et al.  The String-to-String Correction Problem , 1974, JACM.

[13]  Horst Bunke,et al.  Applications of approximate string matching to 2D shape recognition , 1993, Pattern Recognit..

[14]  Cristian Sminchisescu,et al.  Covariance scaled sampling for monocular 3D body tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Cristian Sminchisescu,et al.  Twin Gaussian Processes for Structured Prediction , 2010, International Journal of Computer Vision.

[16]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[17]  Alexander M. Bronstein,et al.  Numerical Geometry of Non-Rigid Shapes , 2009, Monographs in Computer Science.

[18]  Jitendra Malik,et al.  Twist Based Acquisition and Tracking of Animal and Human Kinematics , 2004, International Journal of Computer Vision.

[19]  Bodo Rosenhahn,et al.  Multisensor-fusion for 3D full-body human motion capture , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Noel E. O'Connor,et al.  Efficient contour-based shape representation and matching , 2003, MIR '03.

[21]  Hans-Peter Seidel,et al.  Optimization and Filtering for Human Motion Capture , 2010, International Journal of Computer Vision.

[22]  Cristian Sminchisescu.  Consistency and coupling in human model likelihoods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[23]  Andrés Marzal,et al.  Dynamic Time Warping of Cyclic Strings for Shape Matching , 2005, ICAPR.

[24]  David J. Fleet,et al.  3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[25]  Trevor Darrell,et al.  Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[26]  Christopher M. Bishop,et al.  Non-linear Bayesian Image Modelling , 2000, ECCV.

[27]  Yasushi Yagi.  Computer Vision - ACCV 2007, 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part I , 2007, ACCV.

[28]  David Demirdjian.  Combining Geometric- and View-Based Approaches for Articulated Pose Estimation , 2004, ECCV.

[29]  Emiliano Gambaretto,et al.  Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation , 2010, International Journal of Computer Vision.

[30]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Bodo Rosenhahn,et al.  Ball joints for Marker-less human Motion Capture , 2009, 2009 Workshop on Applications of Computer Vision (WACV).