Abstract
In this paper we present an approach to use prior knowledge in the particle filter framework for 3D tracking, i.e. estimating the state parameters such as joint angles of a 3D object. The probability of the object’s states, including correlations between the state parameters, is learned a priori from training samples. We introduce a framework that integrates this knowledge into the family of particle filters and particularly into the annealed particle filter scheme. Furthermore, we show that the annealed particle filter also works with a variational model for level set based image segmentation that does not rely on background subtraction and, hence, does not depend on a static background. In our experiments, we use a four camera set-up for tracking the lower part of a human body by a kinematic model with 18 degrees of freedom. We demonstrate the increased accuracy due to the prior knowledge and the robustness of our approach to image distortions. Finally, we compare the results of our multi-view tracking system quantitatively to the outcome of an industrial marker based tracking system.
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References
Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. In: Statistics for Engineering and Information Science. Springer, New York (2001)
Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. Int. J. of Computer Vision 29, 5–28 (1998)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. Int. J. of Computer Vision 61, 185–205 (2005)
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)
Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)
Sminchisescu, C., Triggs, B.: Estimating articulated human motion with covariance scaled sampling. Int. J. of Robotics Research 22, 371–391 (2003)
Sminchisescu, C., Jepson, A.: Generative modeling for continuous non-linearily embedded visual inference. In: Int. Conf. on Machine Learning (2004)
Brox, T., Rosenhahn, B., Kersting, U.G., Cremers, D.: Nonparametric density estimation for human pose tracking. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 546–555. Springer, Heidelberg (2006)
Brox, T., Rousson, M., Deriche, R., Weickert, J.: Unsupervised segmentation incorporating colour, texture, and motion. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 353–360. Springer, Heidelberg (2003)
Rosenhahn, B., Brox, T., Smith, D., Gurney, J., Klette, R.: A system for marker-less human motion estimation. Künstliche Intelligenz 1, 45–51 (2006)
Crisan, D., Doucet, A.: A survey of convergence results on particle filtering methods for practitioners. IEEE Transaction on Signal Processing 50, 736–746 (2002)
Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Brox, T., Rosenhahn, B., Weickert, J.: Three-dimensional shape knowledge for joint image segmentation and pose estimation. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 109–116. Springer, Heidelberg (2005)
Parzen, E.: On estimation of a probability density function and mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)
Silverman, B.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)
Mukundan, R., Ramakrishnan, K.: Moment Functions in Image Analysis: Theory and Application. World Scientific Publishing, Singapore (1998)
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Gall, J., Rosenhahn, B., Brox, T., Seidel, HP. (2006). Learning for Multi-view 3D Tracking in the Context of Particle Filters. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_7
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DOI: https://doi.org/10.1007/11919629_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48626-8
Online ISBN: 978-3-540-48627-5
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