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39
Sequential Monte Carlo Methods for Dynamic Systems
 Journal of the American Statistical Association
, 1998
"... A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ..."
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Cited by 608 (10 self)
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A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We deliver a guideline on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of RaoBlackwellization to improve performances. Examples from econometrics and engineering are presented to demonstrate the importance of RaoBlackwellization and to compare different Monte Carlo procedures. Keywords: Blind deconvolution; Bootstrap filter; Gibbs sampling; Hidden Markov model; Kalman filter; Markov...
Icondensation: Unifying lowlevel and highlevel tracking in a stochastic framework
, 1998
"... . Tracking research has diverged into two camps; lowlevel approaches which are typically fast and robust but provide little finescale information, and highlevel approaches which track complex deformations in highdimensional spaces but must trade off speed against robustness. Realtime highlevel ..."
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Cited by 302 (13 self)
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. Tracking research has diverged into two camps; lowlevel approaches which are typically fast and robust but provide little finescale information, and highlevel approaches which track complex deformations in highdimensional spaces but must trade off speed against robustness. Realtime highlevel systems perform poorly in clutter and initialisation for most highlevel systems is either performed manually or by a separate module. This paper presents a new technique to combine low and highlevel information in a consistent probabilistic framework, using the statistical technique of importance sampling combined with the Condensation algorithm. The general framework, which we term Icondensation, is described, and a hand tracker is demonstrated which combines colour blobtracking with a contour model. The resulting tracker is robust to rapid motion, heavy clutter and handcoloured distractors, and reinitialises automatically. The system runs comfortably in real time on an...
An Improved Particle Filter for Nonlinear Problems
, 2004
"... The Kalman filter provides an effective solution to the linearGaussian filtering problem. However, where there is nonlinearity, either in the model specification or the observation process, other methods are required. We consider methods known generically as particle filters, which include the c ..."
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Cited by 252 (10 self)
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The Kalman filter provides an effective solution to the linearGaussian filtering problem. However, where there is nonlinearity, either in the model specification or the observation process, other methods are required. We consider methods known generically as particle filters, which include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters
An Adaptive ColorBased Particle Filter
, 2002
"... Robust realtime tracking of nonrigid objects is a challenging task. Particle filtering has proven very successful for nonlinear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination wi ..."
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Cited by 141 (5 self)
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Robust realtime tracking of nonrigid objects is a challenging task. Particle filtering has proven very successful for nonlinear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edgebased image features. Color distributions are applied, as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. As the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters, the target model is adapted during temporally stable image observations. An initialization based on an appearance condition is introduced since tracked objects may disappear and reappear. Comparisons with the mean shift tracker and a combination between the mean shift tracker and Kalman filtering show the advantages and limitations of the new approach.
Estimating Articulated Human Motion With Covariance Scaled Sampling
 International Journal of Robotics Research
, 2003
"... We present a method for recovering 3D human body motion from monocular video sequences based on a robust image matching metric, incorporation of joint limits and nonselfintersection constraints, and a new sampleandrefine search strategy guided by rescaled costfunction covariances. Monocular 3D ..."
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Cited by 119 (10 self)
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We present a method for recovering 3D human body motion from monocular video sequences based on a robust image matching metric, incorporation of joint limits and nonselfintersection constraints, and a new sampleandrefine search strategy guided by rescaled costfunction covariances. Monocular 3D body tracking is challenging: besides the difficulty of matching an imperfect, highly flexible, selfoccluding model to cluttered image features, realistic body models have at least 30 joint parameters subject to highly nonlinear physical constraints, and at least a third of these degrees of freedom are nearly unobservable in any given monocular image. For image matching we use a carefully designed robust cost metric combining robust optical flow, edge energy, and motion boundaries. The nonlinearities and matching ambiguities make the parameterspace cost surface multimodal, illconditioned and highly nonlinear, so searching it is difficult. We discuss the limitations of CONDENSATIONlike samplers, and describe a novel hybrid search algorithm that combines inflatedcovariancescaled sampling and robust continuous optimization subject to physical constraints and model priors. Our experiments on challenging monocular sequences show that robust cost modeling, joint and selfintersection constraints, and informed sampling are all essential for reliable monocular 3D motion estimation.
Gaussian particle filtering
 IEEE Transactions on Signal Processing
, 2003
"... Abstract—Sequential Bayesian estimation for nonlinear dynamic statespace models involves recursive estimation of filtering and predictive distributions of unobserved time varying signals based on noisy observations. This paper introduces a new filter called the Gaussian particle filter1. It is base ..."
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Cited by 80 (5 self)
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Abstract—Sequential Bayesian estimation for nonlinear dynamic statespace models involves recursive estimation of filtering and predictive distributions of unobserved time varying signals based on noisy observations. This paper introduces a new filter called the Gaussian particle filter1. It is based on the particle filtering concept, and it approximates the posterior distributions by single Gaussians, similar to Gaussian filters like the extended Kalman filter and its variants. It is shown that under the Gaussianity assumption, the Gaussian particle filter is asymptotically optimal in the number of particles and, hence, has muchimproved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present. Simulation results are presented to demonstrate the versatility and improved performance of the Gaussian particle filter over conventional Gaussian filters and the lower complexity than known particle filters. The use of the Gaussian particle filter as a building block of more complex filters is addressed in a companion paper. Index Terms—Dynamic state space models, extended Kalman filter, Gaussian mixture, Gaussian mixture filter, Gaussian particle filter, Gaussian sum filter, Gaussian sum particle filter, Monte Carlo filters, nonlinear nonGaussian stochastic systems, particle filters, sequential Bayesian estimation, sequential sampling methods, unscented Kalman filter. I.
Gaussian sum particle filtering
 Signal Processing 51
, 2003
"... Abstract—In this paper, we use the Gaussian particle filter introduced in a companion paper to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters ..."
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Cited by 63 (3 self)
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Abstract—In this paper, we use the Gaussian particle filter introduced in a companion paper to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with nonGaussian noise. With nonGaussian noise approximated by Gaussian mixtures, the nonGaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. 1 As a result, problems involving heavytailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined. Index Terms—Dynamic statespace models, extended Kalman
A hybrid bootstrap filter for target tracking in clutter
 IEEE Transactions on Aerospace and Electronic Systems
, 1997
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Tracking Multiple Objects Using the Condensation Algorithm
, 2001
"... Some years ago a new tracker, the Condensation algorithm, came... In this paper an extension of the Condensation algorithm is introduced that relies on a single probability distribution to describe the likely states of multiple objects. By introducing an initialization density, observations can ow d ..."
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Cited by 38 (0 self)
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Some years ago a new tracker, the Condensation algorithm, came... In this paper an extension of the Condensation algorithm is introduced that relies on a single probability distribution to describe the likely states of multiple objects. By introducing an initialization density, observations can ow directly into the tracking process, such that newly appearing objects can be handled
Visual Motion Analysis by Probabilistic Propagation of Conditional Density
, 1998
"... This thesis establishes a stochastic framework for tracking curves in visual clutter, using a Bayesian randomsampling algorithm. The approach is rooted in ideas from statistics, control theory and computer vision. The problem is to track outlines and features of foreground objects, modelled as curv ..."
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Cited by 33 (0 self)
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This thesis establishes a stochastic framework for tracking curves in visual clutter, using a Bayesian randomsampling algorithm. The approach is rooted in ideas from statistics, control theory and computer vision. The problem is to track outlines and features of foreground objects, modelled as curves, as they move in substantial clutter, and to do it at, or close to, video framerate. The algorithm, named Condensation, for Conditional density propagation, has recently been derived independently by several researchers, and is generating signi cant interest in the statistics and signal processing communities. This thesis contributes to the literature on Condensationlike lters by presenting some novel applications of and extensions to the basic algorithm, and contributes to the visual motion estimation literature by demonstrating high tracking performance in cluttered environments. Despite its power the Condensation algorithm has a remarkably simple form and this allows the use of nonlinear motion models which combine characteristics of discrete Hidden Markov Models with the continuous AutoRegressive Process motion models traditionally used in Kalman lters. These mixed discretecontinuous models have promising applications to the emerging eld of perception of action. This thesis also implements two algorithms to smooth the output of the Condensation lter which improves the accuracy of motion estimation in a batchmode procedure after tracking is complete.