Abstract
In this paper we construct a novel human body model using convolution surface with articulated kinematic skeleton. The human body’s pose and shape in a monocular image can be estimated from convolution curve through nonlinear optimization. The contribution of the paper is in three folds: Firstly, human model based convolution surface with articulated skeletons is presented and its shape is deformable when changing polynomial parameters and radius parameters. Secondly, we give convolution surface and curve correspondence theorem under weak perspective projection, which provide a bridge between the 3D pose and 2D contour. Thirdly, we model the human body’s silhouette with convolution curve in order to estimate joint’s parameters from monocular images. Evalution of the method is performed on a sequence of video frames about a walking man.
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References
Bloomenthal, J., Shoemake, K.: Convolution surface. Computer Graphics 25(4), 251–256 (1991)
DeCarlo, D., Metaxas, D.: Blended Deformable models. PAMI 18(4), 443–448 (1996)
Jin, X., Tai, C.-L.: Convolution Surfaces for Line Skeletons with Polynomial Weight Distributions. Journal of Graphics Tools ACM Press 6(3), 17–28 (2001)
Tai, C.-L., Zhang, H., Fong, C.-K.: Prototype Modeling from Sketched Silhouettes based on Convolution Surfaces. Computer Graphics Forum 23(1) (2004)
Moeslund, T.B., Granum, E.: A Survey of Computer Vision-Based Human Motion Capture. Int. Journal of Computer Vision and Image Under-standing 81(3), 231–268 (2001)
Aggarwal, J.K., Cai, Q., Liao, W., Sabatay, B.: Non-rigid Motion Analysis: Articulated and Elastic Motion. Int. Journal of Computer Vision and Image Understanding 70(2), 142–156 (1998)
Gavrila, D.M.: The visual analysis of human movement: a survey. Computer Vision and Image Understanding 73(1), 82–98 (1999)
Shoji, K., Mito, A., Toyama, F.: Pose estimation of a 2-D articulated objects from its silhouette. In: Proc. 15th Int. Conf. On Pattern Recognition, vol. 3, pp. 713–717 (2000)
Ju, S.X., Black, M.J., Yacoob, Y.: Cardboard people: a parameterized model of articulated image motion. In: Proc. 2nd Int. Conf. On Automatic Face and Gesture Recognition, pp. 38–44 (1996)
Gavrila, D., Davis, L.: 3-D model-based tracking of human in action: a multi-view approach. In: CVPR San Francisco, pp. 73–80 (1996)
Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: CVPR, pp. 8–15 (1998)
Bregler, C., Aaron, H., Biermann, H.: Recovering Non-Rigid 3D Shape from Image Streams. In: CVPR, vol. 2 (2000)
Sand, P., McMillan, L., Popovic, J.: Continuous Capture of Skin Deformation. In: SIGGRAPH (2003)
Plankers, R., Fua, P.: Articulated soft objects for multi-view shape and motion capture. PAMI 25(10) (2003)
Sminchisescu, C., Telea, A.: Human pose estimation from silhouettes. a consistent approach using distance level sets. In: WSCG International Conference on Computer Graphics, Visualization and Computer Vision (2002)
Sherstyuk, A.: Kernel functions in convolution surfaces: A comparative analysis. The Visual Computer 15(4), 171–182 (1999)
Moré, J.J., Sorensen, D.C.: Computing a Trust Region Step. SIAM Journal on Scientific and Statistical Computing 3, 553–572 (1983)
Steihaug, T.: The Conjugate Gradient Method and Trust Regions in Large Scale Optimization. SIAM Journal on Numerical Analysis 20, 626–637 (1983)
Tolani, D., Goswami, A., Badler, N.: Real-Time Inverse Kinematics Techniques for Anthropometric Limbs. Graphical Models 62, 338–353 (2000)
Zhang, X., Liu, Y., Huang, T.S.: Motion Estimation of Articulated Objects from Perspective View. In: Perales, F.J., Hancock, E.R. (eds.) AMDO 2002. LNCS, vol. 2492, pp. 165–176. Springer, Heidelberg (2002)
Yang, Y., Levine, M.: The Background Primal Sketch: An Approach for Tracking Moving Objects. Machine Vision and Applications 5, 17–34 (1992)
Kuno, Y., Watanabe, T., Shimosakoda, Y., Nakagawa, S.: Automated Detection of Human for Visual Surveillance System. In: Proc. Int’l Conf. Pattern Recognition, pp. 865–869 (1996)
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Tong, M., Liu, Y., Huang, T.S. (2005). Recover Human Pose from Monocular Image Under Weak Perspective Projection. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_4
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DOI: https://doi.org/10.1007/11573425_4
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