Abstract
To obtain semantic interpretations of object actions and behaviors is a challenging application of 3D video. This chapter presents a method of computing a kinematic motion description from a 3D video stream of complex human action such as Yoga. In general, appropriate knowledge should be given a priori to obtain a semantic description of physical data. Here a kinematic model is defined as a skeleton structure consisting of bones and joints, while the kinematic description includes characterizations such as how much a joint angle between a pair of connected bones changes over time. The key idea of the presented algorithm is to introduce a pair of reliability measures into the kinematic model matching: surface visibility and photo-consistency measures. The algorithm realizes robust model matching against substantial self-occlusions and enables us to measure 3D kinematic human motion without any markers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, A., Triggs, B.: 3d human pose from silhouettes by relevance vector regression. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR’04, pp. 882–888 (2004)
Agarwal, A., Triggs, B.: Learning to track 3D human motion from silhouettes. In: Proc. of International Conference on Machine Learning, pp. 9–16 (2004)
Agarwal, A., Triggs, B.: Recovering 3D human pose from monocular images. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 44–58 (2006)
Brand, M.: Shadow puppetry. In: Proc. of International Conference on Computer Vision, vol. 2, pp. 1237–1244 (1999)
Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR’98, p. 8 (1998)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. Int. J. Comput. Vis. 61, 185–205 (2005)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61, 55–79 (2005)
Fernando, R.: GPU Gems: Programming Techniques, Tips and Tricks for Real-Time Graphics. Pearson Higher Education, Upper Saddle River (2004). 0321228324
Gall, J., Stoll, C., Aguiar, E.D., Theobalt, C., Rosenhahn, B., Seidel, H.-P.: Motion capture using joint skeleton tracking and surface estimation. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2009)
Gibson, S., Mirtich, B.: A Survey of Deformable Modeling in Computer Graphics (1997)
Grauman, K., Shakhnarovich, G., Darrell, T.: Inferring 3D structure with a statistical image-based shape model. In: Proc. of International Conference on Computer Vision, pp. 641–647 (2003)
Howe, N.R.: Silhouette lookup for automatic pose tracking. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 15–22 (2004)
King, B.A., Paulson, L.D.: Motion capture moves into new realms. Computer 40, 13–16 (2007)
Ménier, C., Boyer, E., Raffin, B.: 3D Skeleton-Based Body Pose Recovery. In: Proc. of International Symposium on 3D Data Processing, Visualization and Transmission, pp. 389–396 (2006)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006)
Mukasa, T., Miyamoto, A., Nobuhara, S., Maki, A., Matsuyama, T.: Complex human motion estimation using visibility. In: Proc. of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–6 (2008)
Nobuhara, S., Miyamoto, A., Matsuyama, T.: Complex 3D human motion estimation by modeling incompleteness in 3D shape observation. IEICE Trans. Inf. Syst. J92-D(12), 2225–2237 (2009) (in Japanese)
Ogawara, K., Li, X., Ikeuchi, K.: Marker-less human motion estimation using articulated deformable model. In: Proc. of International Conference on Robotics and Automation, pp. 46–51 (2007)
Peng, B., Qian, G.: Online gesture spotting from visual hull data. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1175–1188 (2011)
Plänkers, R., Fua, P.: Articulated soft objects for multiview shape and motion capture. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1182–1187 (2003)
Rogez, G., Orrite, C., Martinez, J., Herrero, J.: Probabilistic spatio-temporal 2D-model for pedestrian motion analysis in monocular sequences. In: Proc. of International Conference on Articulated Motion and Deformable Objects, pp. 175–184 (2006)
Rosales, R., Siddiqui, M., Alon, J., Sclaroff, S.: Estimating 3D body pose using uncalibrated cameras. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 821–827 (2001)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proc. of International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)
Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter-sensitive hashing. In: Proc. of International Conference on Computer Vision, pp. 750–757 (2003)
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3D human motion estimation. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 390–397 (2005)
Starck, J., Hilton, A.: Spherical matching for temporal correspondence of non-rigid surfaces. In: Proc. of International Conference on Computer Vision, vol. 2, pp. 1387–13942 (2005)
Theobalt, C., Magnor, M., Schuler, P., Seidel, H.-P.: Multi-layer skeleton fitting for online human motion capture. In: Proceedings of 7th International Fall Workshop on Vision, Modeling and Visualization, pp. 471–478 (2002)
Ukita, N., Hirai, M., Kidode, M.: Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints. In: Proc. of International Conference on Computer Vision, pp. 1405–1412 (2009)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London
About this chapter
Cite this chapter
Matsuyama, T., Nobuhara, S., Takai, T., Tung, T. (2012). Model-Based Complex Kinematic Motion Estimation. In: 3D Video and Its Applications. Springer, London. https://doi.org/10.1007/978-1-4471-4120-4_9
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4120-4_9
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4119-8
Online ISBN: 978-1-4471-4120-4
eBook Packages: Computer ScienceComputer Science (R0)