Particle+Filter

= Particle Filters =
 * 1) == Introduction and Tutorials ==
 * Particle filter, Wikipedia, 2011/2/5.
 * M. S. Arulampalam et. al. (2002), [|A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking] . Accessible particle filter tutorial with pseudocode for several variants.
 * 1) == **Application** ==
 * ===== Object Tracking =====
 * Kernel-based
 * Bohyung Han, Ying Zhu, Dorin Comaniciu and Larry Davis, __ [|//Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework//] __, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31(5), 919-930, 2009
 * Bohyung Han, Dorin Comaniciu, Ying Zhu and Larry Davis, __ [|//Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking//] __, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30(7), 1186-1197, 2008
 *  J. H. Kotecha and P. M. Djuric, "Gaussian sum particle filtering", //IEEE Transactions on Signal Process.//, vol. 51, no. 10, pp.2602 - 2610 , 2003.
 *  <span class="Apple-style-span" style="-webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; border-collapse: separate; color: #333333; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">Merwe, R. V. D., Doucet, A., Freitas, N. D., & Wan, E. (2001). The Unscented Particle Filter. (T. K. Leen, T. G. Dietterich, & V. Tresp, Eds.)//Advances in Neural Information Processing Systems//, //volume13//(CUED/F-INFENG/TR 380), 584–590. MIT; 1998. Retrieved from http://www.markirwin.net/stat220/Refs/upf2000.pdf
 * high-order Markov chain
 *   P. Pan and D. Schonfeld, "Video tracking based on sequential particle filtering on graphs," //IEEE Transactions on Image Processing//, accepted.,2011
 *  Meanshift with Particle Filter
 * Khan, Zulfiqar H.; Gu, Irene Y.H.; Backhouse, Andrew: __ [|Robust Visual Object Tracking using Multi-Mode Anisotropic Mean Shift and Particle Filters] __. IEEE Transactions on Circuits and Systems for Video Technology, 21 (1) pp. 74-87.
 * Haner, Sebastian; Gu, Irene Y.H.: __ [|Combining Foreground / Background Feature Points and Anisotropic Mean Shift For Enhanced Visual Object Tracking] __. 20th International Conf. Pattern Recognition (ICPR 2010), 23-26 August, 2010, Istanbul, Turkey, pp. 3488-3491. ISBN/ISSN: 978-076954109-9
 * Others
 * <span class="Apple-style-span" style="-webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; border-collapse: collapse; color: #000000; font-size-adjust: none; font-stretch: normal; font: 13px/normal Arial,Helvetica,sans-serif; letter-spacing: normal; orphans: 2; text-align: justify; text-indent: -36px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">W. Qu, D. Schonfeld, and M. Mohamed, "Real-time distributed multi-object tracking using multiple interactive trackers and a magnetic-inertia potential model," //IEEE Transactions on Multimedia//, vol. 9, pp. 511-519, 2007
 * 1) == Method ==
 * [|The Condensation Algorithm] . Visual tracking using particle filters.
 * <span style="background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; font-family: Arial; line-height: normal; padding-right: 10px;">[|The Kalman Filter] . Wikipedia article describing linear Kalman filtering, as well as nonlinear extensions.
 * A. Doucet et. al. (2001), <span style="background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; padding-right: 10px;">[|Sequential Monte Carlo Methods in Practice] . This edited volume nicely surveys the particle filtering literature.
 * Cappe, O., Godsill, S., and Moulines, E. <span style="background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; padding-right: 10px;">[|An overview of existing methods and recent advances in sequential Monte Carlo], //IEEE Proceedings//, 95(5):899-924, 2007.
 * Doucet, A., Lecture notes on <span style="background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; padding-right: 10px;">[|Importance sampling and sequential importance sampling], 2008.
 * Doucet, A., Lecture notes on <span style="background-position: 100% 50%; background-repeat: no-repeat no-repeat; cursor: pointer; padding-right: 10px;">[|Sequential importance sampling resampling], 2008.
 * 1) == Library/Open Source ==
 * 2) [C] Rob Hess [] [2006 or 2009?]
 * Introduce: This program is implemented in C depending on OpenCV and GSL. It uses a color histogram-based observation model and a second-order autoregressive dynamical model to track a single-object.
 * Reference Papers:
 * <span style="list-style-type: square; margin-bottom: 5px; margin-left: 35px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-top: 0px; width: 921px;">__ [|Object Tracking with Particle Filtering] __ [an old course project presentation that describes this particle filter implementation]
 * <span style="list-style-type: square; margin-bottom: 5px; margin-left: 35px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-top: 0px; width: 921px;">__ [|Color-Based Probabilistic Tracking] __. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. ECCV, 2002.
 * <span style="list-style-type: square; margin-bottom: 5px; margin-left: 35px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-top: 0px; width: 921px;">__ [|Sequential Monte Carlo Methods in Practice] __. A. Doucet, N. de Freitas, and N. Gordon (eds.). Springer, 2001.
 * <span style="list-style-type: square; margin-bottom: 5px; margin-left: 35px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-top: 0px; width: 921px;">__ [|Discriminatively Trained Particle Filters for Complex Multi-Object Tracking] __. R. Hess and A. Fern. CVPR, 2009.
 * 1) [C] OpenCVX - [] [v1.0pre 2009.03]
 * Introduce: An OpenCV extensional library for object tracking which is using particle filtering.
 * 1) [C] MRPT - [] [v0.9.3 2010.12]
 * Introduces: The Mobile Robot Programming Toolkit(MRPT) is a project working for mobile robotics research areas: localization, simultaneous localization and mapping (SLAM), computer vision and motion planning. It is a C++ library. Particle filter is implement in it ([]).
 * 1) [C] Bayes++ - [] [2010.8]
 * Introduce: Open source Bayesian filtering classes by Michael Stevens.
 * 1) [M] PFLib -
 * Introduce: An object oriented MATLAB Toolbox for particle filtering
 * Reference: []
 * 1) [C] Alexander Gruenstein - []
 * Introduce: This project uses the condensation algorithm coded in C++.
 * 1) [C] Regularised Particle Filter - []
 * Reference: <span style="border-collapse: separate; color: #000000; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">P. Fearnhead (2005). Using Random Quasi-Monte-Carlo within Particle Filters, with Application to Financial Time Series. //Journal of Computational and Graphical Statistics//, **14** 751-769.