Abstract
In this paper, we improve the Partitioned Sampling (PS) scheme to better handle high-dimensional state spaces. PS can be explained in terms of conditional independences between random variables of states and observations. These can be modeled by Dynamic Bayesian Networks. We propose to exploit these networks to determine conditionally independent subspaces of the state space. This allows us to simultaneously perform propagations and corrections over smaller spaces. This results in reducing the number of necessary resampling steps and, in addition, in focusing particles into high-likelihood areas. This new methodology, called Simultaneous Partitioned Sampling, is successfully tested and validated for articulated object tracking.
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References
Bernier, O., Cheungmonchan, P., Bouguet, A.: Fast nonparametric belief propagation for real-time stereo articulated body tracking. Computer Vision and Image Understanding 113(1), 29–47 (2009)
Bray, M., Koller-Meier, E., Müller, P., Schraudolph, N.N., Van Gool, L.: Stochastic optimization for high-dimensional tracking in dense range maps. IEE Proceedings Vision, Image and Signal Processing 152(4), 501–512 (2005)
Chang, W.Y., Chen, C.S., Jian, Y.D.: Visual tracking in high-dimensional state space by appearance-guided particle filtering. IEEE Transactions on Image Processing 17(7), 1154–1167 (2008)
Chen, Z.: Bayesian filtering: from kalman filters to particle filters, and beyond (2003)
Deutscher, J., Davison, A., Reid, I.: Automatic partitioning of high dimensional search spaces associated with articulated body motion capture. In: CVPR, vol. 2, pp. 669–676 (2005)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE Proceedings of Radar and Signal Processing 140(2), 107–113 (1993)
Hauberg, S., Sommer, S., Pedersen, K.: Gaussian-like spatial priors for articulated tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 425–437. Springer, Heidelberg (2010)
Hauberg, S., Pedersen, K.S.: Stick it! articulated tracking using spatial rigid object priors. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 758–769. Springer, Heidelberg (2011)
Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)
MacCormick, J.: Probabilistic modelling and stochastic algorithms for visual localisation and tracking. Ph.D. thesis, Oxford University (2000)
MacCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. In: ICCV, pp. 572–587 (1999)
MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, UC Berkeley, Computer Science Division (2002)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman Publishers, Inc., San Francisco (1988)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Qu, W., Schonfeld, D.: Real-time decentralized articulated motion analysis and object tracking from videos. IEEE Transactions on Image Processing 16(8), 2129–2138 (2007)
Rose, C., Saboune, J., Charpillet, F.: Reducing particle filtering complexity for 3D motion capture using dynamic bayesian networks. In: AAAI, pp. 1396–1401 (2008)
Sánchez, A., Pantrigo, J., Gianikellis, K.: Combining Particle Filter and Population-based Metaheuristics for Visual Articulated Motion Tracking. Electronic Letters on Computer Vision and Image Analysis 5(3), 68–83 (2005)
Shen, C., van den Hengel, A., Dick, A., Brooks, M.: 2D articulated tracking with dynamic Bayesian networks. In: Das, G., Gulati, V.P. (eds.) CIT 2004. LNCS, vol. 3356, pp. 130–136. Springer, Heidelberg (2004)
Smith, K., Gatica-perez, D.: Order matters: a distributed sampling method for multi-object tracking. In: BMVC, pp. 25–32 (2004)
Widynski, N., Dubuisson, S., Bloch, I.: Introducing fuzzy spatial constraints in a ranked partitioned sampling for multi-object tracking. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 393–404. Springer, Heidelberg (2010)
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Gonzales, C., Dubuisson, S., N’Guyen, X.S. (2011). Simultaneous Partitioned Sampling for Articulated Object Tracking. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_14
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DOI: https://doi.org/10.1007/978-3-642-23687-7_14
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