Human Body Posture Inference for Immersive Interaction

In this paper we present an approach for inferring the body posture from a 3D visual-hull representation. We present an appearancebased, view-independent, 3D shape description for classifying and identifying human posture using a support vector machine. The proposed global representation allows a robust description of shape that accommodates for variation of the shape of the human body across multiple people. The proposed method does not require an articulated body model fitted onto the reconstructed 3D geometry of the human body: It complements the articulated body model since we can define a mapping between the observed shape and the learned descriptions for inferring the articulated body model. The proposed method is illustrated on a set of body postures captured by four cameras.

[1]  Martial Hebert,et al.  Efficient multiple model recognition in cluttered 3-D scenes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[2]  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.

[3]  Soon Ki Jung,et al.  Particle filter with analytical inference for human body tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[4]  Taku Yamazaki,et al.  Invariant histograms and deformable template matching for SAR target recognition , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Kazuhiko Takahashi,et al.  Human body postures from trinocular camera images , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[6]  Nicholas Ayache,et al.  Tracking Points on Deformable Objects Using Curvature Information , 1992, ECCV.

[7]  Ali Shokoufandeh,et al.  Shock Graphs and Shape Matching , 1998, International Journal of Computer Vision.

[8]  Matthew Turk,et al.  Perceptual user interfaces , 2000 .

[9]  James M. Rehg,et al.  Reconstruction of 3D figure motion from 2D correspondences , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Takeo Kanade,et al.  The 3D Room: Digitizing Time-Varying 3D Events by Synchronized Multiple Video Streams , 1998 .

[12]  Ioannis A. Kakadiaris,et al.  Three-Dimensional Human Body Model Acquisition from Multiple Views , 1998, International Journal of Computer Vision.

[13]  Naoufel Werghi,et al.  Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[14]  Ruzena Bajcsy,et al.  Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Pascal Fua,et al.  Modeling People Toward Vision-Based Understanding of a Person's Shape, Appearance, and Movement , 2001, Comput. Vis. Image Underst..

[16]  Jitendra Malik,et al.  Matching Shapes , 2001, ICCV.

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[18]  A. Laurentini,et al.  The Visual Hull Concept for Silhouette-Based Image Understanding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Mun Wai Lee,et al.  3D Body Reconstruction for Immersive Interaction , 2002, AMDO.

[20]  Isabelle Herlin,et al.  Curves matching using geodesic paths , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).