Abstract
In this paper, we propose to better estimate high-dimensional distributions by exploiting conditional independences within the Particle Filter (PF) framework. We first exploit Dynamic Bayesian Networks to determine conditionally independent subspaces of the state space, which allows us to independently perform propagations and corrections over smaller spaces. Second, we propose a swapping process to transform the weighted particle set provided by the update step of PF into a “new particle set” better focusing on high peaks of the posterior distribution. This new methodology, called Swapping-Based Partitioned Sampling, is successfully tested and validated for articulated object tracking.
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References
Bandouch, J., Engstler, F., Beetz, M.: Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation. In: BMVC, pp. 925–934 (2008)
Bernier, O., Cheung-Mon-Chan, P., Bouguet, A.: Fast nonparametric belief propagation for real-time stereo articulated body tracking. Computer Vision and Image Understanding 113, 29–47 (2009)
Besada-Portas, E., Plis, S.M., Cruz, J.M., Lane, T.: Parallel subspace sampling for particle filtering in dynamic Bayesian networks. In: ECML PKDD, pp. 131–146 (2009)
Chen, Z.: Bayesian filtering: from Kalman filters to particle filters, and beyond (2003)
Doucet, A., de Freitas, N., Murphy, K.P., Russell, S.J.: Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: UAI, pp. 176–183 (2000)
Gordon, N., Salmond, D.J., Smith, A.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings of Radar and Signal Processing 140(2), 107–113 (1993)
Ihler, A., Fisher, J., Iii, J.W.F., Willsky, A., Moses, R.: Nonparametric belief propagation for self-calibration in sensor networks. In: ISIPSN, pp. 225–233 (2004)
Isard, M.: PAMPAS: real-valued graphical models for computer vision. In: CVPR, pp. 613–620 (2003)
Kanazawa, K., Koller, D., Russell, S.: Stochastic simulation algorithms for dynamic probabilistic networks. In: UAI, pp. 346–351 (1995)
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, San Francisco (1988)
Rose, C., Saboune, J., Charpillet, F.: Reducing particle filtering complexity for 3D motion capture using dynamic Bayesian networks. In: AAAI, pp.1396–1401 (2008)
Sigal, L., Isard, M., Sigelman, B.H., Black, M.J.: Attractive people: Assembling loose-limbed models using non-parametric belief propagation. In: NIPS, pp. 1539–1546 (2003)
Sudderth, E.B., Ihler, A.T., Isard, M., Freeman, W.T., Willsky, A.S.: Nonparametric belief propagation. Commununications of ACM 53, 95–103 (2010)
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Dubuisson, S., Gonzales, C., Nguyen, X.S. (2011). Swapping-Based Partitioned Sampling for Better Complex Density Estimation: Application to Articulated Object Tracking. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_41
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DOI: https://doi.org/10.1007/978-3-642-23963-2_41
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