Abstract
We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. The pose estimation without initial information is a challenging optimization problem in a high dimensional space and is essential for texture acquisition and initialization of model-based tracking algorithms. Our method is able to recognize the correct object in the case of multiple objects and estimates its pose with a high accuracy. The key component is a particle-based global optimization method that converges to the global minimum similar to simulated annealing. After detecting potential bounded subsets of the search space, the particles are divided into clusters and migrate to the most attractive cluster as the time increases. The performance of our approach is verified by means of real scenes and a quantative error analysis for image distortions. Our experiments include rigid bodies and full human bodies.
Our research is funded by the MPC for Visual Computing and Communication.
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
Bray, M., Kohli, P., Torr, P.: Posecut: Simultaneous segmentation and 3d pose estimation of humans using dynamic graph-cuts. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 642–655. Springer, Heidelberg (2006)
Brox, T., Rosenhahn, B., Cremers, D., Seidel, H.P.: High accuracy optical flow serves 3-d pose tracking: Exploiting contour and flow based constraints. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 98–111. Springer, Heidelberg (2006)
Rosenhahn, B., Brox, T., Weickert, J.: Three-dimensional shape knowledge for joint image segmentation and pose tracking. Int. J. of Computer Vision 73(3), 243–262 (2007)
Lowe, D.: Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence 31(3), 355–395 (1987)
Lowe, D.: Fitting parameterized three-dimensional models to images. IEEE Trans. on Pattern Analysis and Machine Intelligence 13(5), 441–450 (1991)
Ansar, A., Daniilidis, K.: Linear pose estimation from points or lines. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(5), 578–589 (2003)
Gall, J., Rosenhahn, B., Seidel, H.P.: Robust pose estimation with 3d textured models. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 84–95. Springer, Heidelberg (2006)
Lepetit, V., Pilet, J., Fua, P.: Point matching as a classification problem for fast and robust object pose estimation. IEEE Conf. on Computer Vision and Pattern Recognition 2, 244–250 (2004)
Besl, P., McKay, N.: A method for registration of 3-d shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. of Computer Vision 13(2), 119–152 (1994)
Gall, J., Potthoff, J., Schnörr, C., Rosenhahn, B., Seidel, H.P.: Interacting and annealing particle systems – mathematics and recipes. J. of Mathematical Imaging and Vision (to appear, 2007)
Kirkpatrick, S., Jr., C.G., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
Gall, J., Rosenhahn, B., Seidel, H.P.: An Introduction to Interacting Simulated Annealing. In: Human Motion - Understanding, Modeling, Capture and Animation, Springer, Heidelberg (to appear, 2007)
Douc, R., Cappe, O., Moulines, E.: Comparison of resampling schemes for particle filtering. In: Int. Symposium on Image and Signal Processing and Analysis, pp. 64–69 (2005)
Moral, P.D.: Feynman-Kac Formulae. In: Genealogical and Interacting Particle Systems with Applications, Springer, New York (2004)
Stolfi, J.: Oriented Projective Geometry: A Framework for Geometric Computation. Academic Press, Boston (1991)
Murray, R., Li, Z., Sastry, S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton, FL (1994)
Pennec, X., Ayache, N.: Uniform distribution, distance and expectation problems for geometric features processing. J. of Mathematical Imaging and Vision 9(1), 49–67 (1998)
Felzenszwalb, P., Huttenlocher, D.: Distance transforms of sampled functions. Technical Report TR2004-1963, Cornell Computing and Information Science (2004)
Gall, J., Rosenhahn, B., Brox, T., Seidel, H.P.: Learning for multi-view 3d tracking in the context of particle filters. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 59–69. Springer, Heidelberg (2006)
Brox, T., Rosenhahn, B., Kersting, U., Cremers, D.: Nonparametric density estimation for human pose tracking. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) Pattern Recognition. LNCS, vol. 4174, pp. 546–555. Springer, Heidelberg (2006)
CMU: Graphics lab motion capture database, http://mocap.cs.cmu.edu/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gall, J., Rosenhahn, B., Seidel, HP. (2007). Clustered Stochastic Optimization for Object Recognition and Pose Estimation. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-74936-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74933-2
Online ISBN: 978-3-540-74936-3
eBook Packages: Computer ScienceComputer Science (R0)