Abstract
This paper presents a patch-based approach for pose estimation from single images using a kernelized density voting scheme. We introduce a boosting-like algorithm that models the density using a mixture of weighted ‘weak’ estimators. The ‘weak’ density estimators and corresponding weights are learned iteratively from a training set, providing an efficient method for feature selection. Given a query image, voting is performed by reference patches similar in appearance to query image patches. Locality in the voting scheme allows us to handle occlusions and reduces the size of the training set required to cover the space of possible poses and appearance. Finally, the pose is estimated as the dominant mode in the density. Multimodality can be handled by looking at multiple dominant modes. Experiments carried out on face and articulated body pose databases show that our patch-based pose estimation algorithm generalizes well to unseen examples, is robust to occlusions and provides accurate pose estimation.
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
The bioid face database, http://www.bioid.com/downloads/facedb/
Curious labs, inc., santa cruz, ca. poser 5 - reference manual (2002)
Agarwal, A., Triggs, B.: Learning to Track 3D Human Motion from Silhouettes. In: Proceedings of the 21st International Conference on Machine Learning, Banff, Canada (July 2004)
Agarwal, A., Triggs, B.: A local basis representation for estimating human pose from cluttered images. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, Springer, Heidelberg (2006)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 484. Springer, Heidelberg (1998)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. International Journal of Computer Vision 61 (June 2005)
Gourier, N., Hall, D., Crowley, J.L.: Estimating face orientation from robust detection of salient facial features. In: Proceedings of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures, Cambridge, UK (2004)
He, X., Yan, S., Hu, Y., Niyogi, P.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)
Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 17–32. Springer, Heidelberg (2004)
Leibe, B., Schiele, B.: Scale invariant object categorization using a scale-adaptive mean-shift search. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 145–153. Springer, Heidelberg (2004)
Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (September 1999)
Murphy, K., Torralba, A., Freeman, W.: Using the forest to see the tree: a graphical model relating features, objects and the scenes (2003)
Ronfard, R., Schmid, C., Triggs, B.: Learning to parse pictures of people. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 700–714. Springer, Heidelberg (2002)
Shakhnarovich, G., Viola, P., Darrell, T.: Fast Pose Estimation with Parameter-Sensitive Hashing. In: Proceedings of the IEEE International Conference on Computer Vision, Nice, France, IEEE Computer Society Press, Los Alamitos (2003)
Sidenbladh, H., Black, M., Fleet, D.: Stochastic Tracking of 3D Human Figures Using 2D Image Motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 702–718. Springer, Heidelberg (2000)
Sigal, L., Isard, M., Sigelman, B., Black, M.: Attractive People: Assembling Loose-Limbed Models using Non-Parametric Belief Propagation. In: Advances in Neural Information Processing Systems, Vancouver, Canada (December 2003)
Sminchiesescu, C., Triggs, B.: Kinematic jump processes for monocular 3d human tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2003)
Sudderth, E.B., Ihler, A.T., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2003)
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Computer Vision and Pattern Recognition, 1991. Proceedings CVPR 1991, IEEE Computer Society Conference on, pp. 586–591. IEEE Computer Society Press, Los Alamitos (1991)
Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: ICCV 2005. Proceedings of the Tenth IEEE International Conference on Computer Vision, Washington, DC, USA, vol. 1, pp. 403–410 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Demirdjian, D., Urtasun, R. (2007). Patch-Based Pose Inference with a Mixture of Density Estimators. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol 4778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75690-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-75690-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75689-7
Online ISBN: 978-3-540-75690-3
eBook Packages: Computer ScienceComputer Science (R0)