Evaluation of Hierarchical Sampling Strategies in 3D Human Pose Estimation, Procedings of the British Machine Vision Conference 2008, pp.925-934, 2008. ,
DOI : 10.5244/C.22.92
Fast nonparametric belief propagation for real-time stereo articulated body tracking, Computer Vision and Image Understanding, vol.113, issue.1, pp.29-47, 2009. ,
DOI : 10.1016/j.cviu.2008.07.001
Parallel subspace sampling for particle filtering, dynamic Bayesian networks. In: ECML PKDD, pp.131-146, 2009. ,
Bayesian filtering: from Kalman filters to particle filters, 2003. ,
Rao-Blackwellised particle filtering for dynamic Bayesian networks, pp.176-183, 2000. ,
Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, vol.140, issue.2, pp.107-113, 1993. ,
DOI : 10.1049/ip-f-2.1993.0015
Nonparametric belief propagation for self-calibration in sensor networks, Proceedings of the third international symposium on Information processing in sensor networks , IPSN'04, pp.225-233, 2004. ,
DOI : 10.1145/984622.984656
PAMPAS: real-valued graphical models for computer vision, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.613-620, 2003. ,
DOI : 10.1109/CVPR.2003.1211410
Stochastic simulation algorithms for dynamic probabilistic networks, pp.346-381, 1995. ,
Probabilistic modelling and stochastic algorithms for visual localisation and tracking, 2000. ,
A probabilistic exclusion principle for tracking multiple objects, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.572-587, 1999. ,
DOI : 10.1109/ICCV.1999.791275
Partitioned sampling, articulated objects, and interfacequality hand tracking, pp.3-19, 2000. ,
Dynamic Bayesian Networks: Representation, Inference and Learning, 2002. ,
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988. ,
Reducing particle filtering complexity for 3D motion capture using dynamic Bayesian networks. AAAI pp, pp.1396-1401, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00332714
Attractive people: Assembling loose-limbed models using non-parametric belief propagation, pp.1539-1546, 2003. ,
Nonparametric belief propagation, Communications of the ACM, vol.53, issue.10, pp.95-103, 2010. ,
DOI : 10.1145/1831407.1831431
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.9687
Zooms on tracking results obtained for PS (top line) and SBPS (bottom line) on frames 50, 100, 200 and 250, for a squid of length P = 5 with N = 5 particles. White articulated objects represent the mean estimations of the articulated object, Mean tracking error: 1454 pixels for PS, and 403 pixels for SBPS ,