Abstract
This paper considers the problem of monocular human body tracking using learned models. We propose to learn the joint probability distribution of appearance and body pose using a mixture of view-dependent models. In such a way the multimodal and nonlinear relationships can be captured reliably. We formulate inference algorithms that are based on generative models while exploiting the advantages of a learned model when compared to the traditionally used geometric body models. Given static images or sequences, body poses and bounding box locations are inferred using silhouette based image descriptors. Prior information about likely body poses and a motion model are taken into account. We consider analytical computations and Monte-Carlo techniques, as well as a combination of both. In a Rao-Blackwellised particle filter, the tracking problem is partitioned into a part that is solved analytically, and a part that is solved with particle filtering. Tracking results are reported for human locomotion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proc. IEEE CVPR (2000)
Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic tracking of 3D human figures using 2D image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)
Sigal, L., Bhatia, S., Roth, S., Black, M., Isard, M.: Tracking loose-limbed people. In: CVPR (2004)
Sminchisescu, C., Triggs, B.: Kinematic jump processes for monocular 3d human tracking. In: CVPR (2003)
Urtasun, R., Fua, P.: 3D human body tracking using deterministic temporal motion models. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 92–106. Springer, Heidelberg (2004)
Shakhnarovich, G., Viola, P., Darrel, T.: Fast pose estimation with parameter sensitive hashing. In: ICCV (2003)
Agarwal, A., Triggs, B.: 3d human pose from silhouettes by relevance vector regression. In: CVPR (2004)
Agarwal, A., Triggs, B.: Monocular human motion capture with a mixture of regressors. In: IEEE Workshop on Vision for Human-Computer Interaction at CVPR (2005)
Elgammal, A., Lee, C.S.: Inferring 3d body pose from silhouettes using activity manifold learning. In: CVPR (2004)
Grauman, K., Shakhnarovich, G., Darrel, T.: Inferring 3d structure with a statistical image-based shape model. In: ICCV (2003)
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3d human motion estimation. In: CVPR (2005)
Rosales, R., Sclaroff, S.: Learning body pose via specialized maps. In: Advances in Neural Information Processing Systems (2001)
Murphy, K., Russel, S.: Rao-blackwellized particle filtering for dynamic bayesian networks. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice, pp. 499–515. Springer, Heidelberg (2001)
Bailey, D.G.: An efficient euclidean distance transform. In: Klette, R., Žunić, J. (eds.) IWCIA 2004. LNCS, vol. 3322, pp. 394–408. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jaeggli, T., Koller-Meier, E., Van Gool, L. (2006). Monocular Tracking with a Mixture of View-Dependent Learned Models. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_51
Download citation
DOI: https://doi.org/10.1007/11789239_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36031-5
Online ISBN: 978-3-540-36032-2
eBook Packages: Computer ScienceComputer Science (R0)