Capturing natural hand articulation

Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.

[1]  Jakub Segen,et al.  Shadow gestures: 3D hand pose estimation using a single camera , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  Ying Wu,et al.  Capturing articulated human hand motion: a divide-and-conquer approach , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Vladimir Pavlovic,et al.  Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Rómer Rosales,et al.  Inferring body pose without tracking body parts , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Thomas S. Huang,et al.  Vision based hand modeling and tracking for virtual teleconferencing and telecollaboration , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  Ying Wu,et al.  View-independent recognition of hand postures , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Tosiyasu L. Kunii,et al.  Model-based analysis of hand posture , 1995, IEEE Computer Graphics and Applications.

[8]  David C. Hogg,et al.  Towards 3D hand tracking using a deformable model , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[9]  Rong Chen,et al.  A Theoretical Framework for Sequential Importance Sampling with Resampling , 2001, Sequential Monte Carlo Methods in Practice.

[10]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[12]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[13]  Takeo Kanade,et al.  Model-based tracking of self-occluding articulated objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[14]  Yoshiaki Shirai,et al.  Hand gesture estimation and model refinement using monocular camera-ambiguity limitation by inequality constraints , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[15]  James M. Rehg,et al.  A multiple hypothesis approach to figure tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[16]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[17]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.