Showing papers on "Particle filter published in 2002"

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

Particle filters for positioning, navigation, and tracking

Abstract: A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter-based algorithms. Here, the use of nonlinear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable with satellite navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.

Color-Based Probabilistic Tracking

Abstract: Color-based trackers recently proposed in [3,4,5] have been proved robust and versatile for a modest computational cost They are especially appealing for tracking tasks where the spatial structure of the tracked objects exhibits such a dramatic variability that trackers based on a space-dependent appearance reference would break down very fast Trackers in [3,4,5] rely on the deterministic search of a window whose color content matches a reference histogram color modelRelying on the same principle of color histogram distance, but within a probabilistic framework, we introduce a new Monte Carlo tracking technique The use of a particle filter allows us to better handle color clutter in the background, as well as complete occlusion of the tracked entities over a few framesThis probabilistic approach is very flexible and can be extended in a number of useful ways In particular, we introduce the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects

A survey of convergence results on particle filtering methods for practitioners

Abstract: Optimal filtering problems are ubiquitous in signal processing and related fields. Except for a restricted class of models, the optimal filter does not admit a closed-form expression. Particle filtering methods are a set of flexible and powerful sequential Monte Carlo methods designed to. solve the optimal filtering problem numerically. The posterior distribution of the state is approximated by a large set of Dirac-delta masses (samples/particles) that evolve randomly in time according to the dynamics of the model and the observations. The particles are interacting; thus, classical limit theorems relying on statistically independent samples do not apply. In this paper, our aim is to present a survey of convergence results on this class of methods to make them accessible to practitioners.

Image segmentation by data-driven Markov chain Monte Carlo

Abstract: This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in four aspects. First, it designs efficient and well-balanced Markov Chain dynamics to explore the complex solution space and, thus, achieves a nearly global optimal solution independent of initial segmentations. Second, it presents a mathematical principle and a K-adventurers algorithm for computing multiple distinct solutions from the Markov chain sequence and, thus, it incorporates intrinsic ambiguities in image segmentation. Third, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which drive the Markov chain dynamics and achieve tremendous speedup in comparison to the traditional jump-diffusion methods. Fourth, the DDMCMC paradigm provides a unifying framework in which the role of many existing segmentation algorithms, such as, edge detection, clustering, region growing, split-merge, snake/balloon, and region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. Thus, the DDMCMC paradigm combines and generalizes these segmentation methods in a principled way. The DDMCMC paradigm adopts seven parametric and nonparametric image models for intensity and color at various regions. We test the DDMCMC paradigm extensively on both color and gray-level images and some results are reported in this paper.

A sequential particle filter method for static models

Abstract: SUMMARY Particle filter methods are complex inference procedures, which combine importance sampling and Monte Carlo schemes in order to explore consistently a sequence of multiple distributions of interest. We show that such methods can also offer an efficient estimation

Particle filters for state-space models with the presence of unknown static parameters

Abstract: Particle filters for dynamic state-space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, real-time applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some low-dimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state-space models. Marginalizing the static parameters avoids the problem of impoverishment, which typically occurs when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results.

Sequential Monte Carlo methods for multiple target tracking and data fusion

Abstract: The classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearings-only context, whereas a JPDAF diverges. Making use of the ability of the particle filter to mix different types of observations, we then investigate how to join passive and active measurements for improved tracking.

Tracking multiple objects with particle filtering

Abstract: We address the problem of multitarget tracking (MTT) encountered in many situations in signal or image processing. We consider stochastic dynamic systems detected by observation processes. The difficulty lies in the fact that the estimation of the states requires the assignment of the observations to the multiple targets. We propose an extension of the classical particle filter where the stochastic vector of assignment is estimated by a Gibbs sampler. This algorithm is used to estimate the trajectories of multiple targets from their noisy bearings, thus showing its ability to solve the data association problem. Moreover this algorithm is easily extended to multireceiver observations where the receivers can produce measurements of various nature with different frequencies.

Implicit Probabilistic Models of Human Motion for Synthesis and Tracking

Abstract: This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical distribution. These methods replace the problem of representing the probability of a texture pattern with that of searching the training data for similar instances of that pattern. We extend this idea to temporal data representing 3D human motion with a large database of example motions. To make the method useful in practice, we must address the problem of efficient search in a large training set; efficiency is particularly important for tracking. Towards that end, we learn a low dimensional linear model of human motion that is used to structure the example motion database into a binary tree. An approximate probabilistic tree search method exploits the coefficients of this low-dimensional representation and runs in sub-linear time. This probabilistic tree search returns a particular sample human motion with probability approximating the true distribution of human motions in the database. This sampling method is suitable for use with particle filtering techniques and is applied to articulated 3D tracking of humans within a Bayesian framework. Successful tracking results are presented, along with examples of synthesizing human motion using the model.

Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering

Abstract: This paper presents algorithms and a prototype system for hand tracking and hand posture recognition. Hand postures are represented in terms of hierarchies of multi-scale colour image features at different scales, with qualitative inter-relations in terms of scale, position and orientation. In each image, detection of multi-scale colour features is performed. Hand states are then simultaneously detected and tracked using particle filtering, with an extension of layered sampling referred to as hierarchical layered sampling. Experiments are presented showing that the performance of the system is substantially improved by performing feature detection in colour space and including a prior with respect to skin colour. These components have been integrated into a real-time prototype system, applied to a test problem of controlling consumer electronics using hand gestures. In a simplified demo scenario, this system has been successfully tested by participants at two fairs during 2001.

Conditional particle filters for simultaneous mobile robot localization and people-tracking

Abstract: Presents a probabilistic algorithm for simultaneously estimating the pose of a mobile robot and the positions of nearby people in a previously mapped environment. This approach, called the conditional particle filter, tracks a large distribution of person locations conditioned upon a smaller distribution of robot poses over time. This method is robust to sensor noise, occlusion, and uncertainty in robot localization. In fact, conditional particle filters can accurately track people in situations with global uncertainty over robot pose. The number of samples required by this filter scales linearly with the number of people being tracked, making the algorithm feasible to implement in real-time in environments with large numbers of people. Experimental results illustrate the accuracy of tracking and model selection, as well as the performance of an active following behavior based on this algorithm.

Tracking a ballistic target: comparison of several nonlinear filters

Abstract: This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer-Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and standard deviation; consistency test) of the following nonlinear filters is compared: the extended Kalman filter (EKF), the. statistical linearization, the particle filtering, and the unscented Kalman filter (UKF). The simulation results favor the EKF; it combines the statistical efficiency with a modest computational load. This conclusion is valid when the target ballistic coefficient is a priori known.

Particle filters in robotics

Abstract: In recent years, particle filters have solved several hard perceptual problems in robotics. Early successes of particle filters were limited to low-dimensional estimation problems, such as the problem of robot localization in environments with known maps. More recently, researchers have begun exploiting structural properties of robotic domains that have led to successful particle filter applications in spaces with as many as 100,000 dimensions. The fact that every model--no mater how detailed--fails to capture the full complexity of even the most simple robotic environments has lead to specific tricks and techniques essential for the success of particle filters in robotic domains. This article surveys some of these recent innovations, and provides pointers to in-depth articles on the use of particle filters in robotics.

Policy recognition in the abstract hidden Markov model

Abstract: In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Object Tracking with an Adaptive Color-Based Particle Filter

Abstract: Color can provide an efficient visual feature for tracking non-rigid objects in real-time. However, the color ofan object can vary over time dependent on the illumination, the visual angle and the camera parameters. To handle these appearance changes a color-based target model must be adapted during temporally stable image observations.This paper presents the integration ofcolor distributions into particle filtering and shows how these distributions can be adapted over time. A particle filter tracks several hypotheses simultaneously and weights them according to their similarity to the target model. As similarity measure between two color distributions the popular Bhattacharyya coefficient is applied. In order to update the target model to slowly varying image conditions, frames where the object is occluded or too noisy must be discarded.

Bayesian Monte Carlo

Abstract: We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensional integrals involved in computing marginal likelihoods of statistical models (a.k.a. partition functions and model evidences). We find that Bayesian Monte Carlo outperformed Annealed Importance Sampling, although for very high dimensional problems or problems with massive multimodality BMC may be less adequate. One advantage of the Bayesian approach to Monte Carlo is that samples can be drawn from any distribution. This allows for the possibility of active design of sample points so as to maximise information gain.

Particle filtering for partially observed Gaussian state space models

Abstract: Summary. Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large number of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems. We propose a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed Gaussian state space models. This algorithm is based on a marginalization idea for improving efficiency and can lead to substantial gains over standard algorithms. It differs from previous algorithms which were only applicable to conditionally linear Gaussian state space models. Computer simulations are carried out to evaluate the performance of the proposed algorithm for dynamic tobit and probit models.

Sequential monte carlo samplers

Abstract: This paper shows how one can use Sequential Monte Carlo methods to perform what is typically done using Markov chain Monte Carlo methods. This leads to a general class of principled integration and genetic type optimization methods based on interacting particle systems.

A Bayesian approach to tracking multiple targets using sensor arrays and particle filters

Abstract: We present a Bayesian approach to tracking the direction-of-arrival (DOA) of multiple moving targets using a passive sensor array. The prior is a description of the dynamic behavior we expect for the targets which is modeled as constant velocity motion with a Gaussian disturbance acting on the target's heading direction. The likelihood function is arrived at by defining an uninformative prior for both the signals and noise variance and removing these parameters from the problem by marginalization. Advances in sequential Monte Carlo (SMC) techniques, specifically the particle filter algorithm, allow us to model and track the posterior distribution defined by the Bayesian model using a collection of target states that can be viewed as samples from the posterior of interest. We describe two versions of this algorithm and finally present results obtained using synthetic data.

An experimental comparison of localization methods continued

Abstract: Localization is one of the fundamental problems in mobile robot navigation. Past experiments have shown that, in general, grid-based Markov localization is more robust than Kalman filtering while the latter can be more accurate than the former Recently new methods for localization employing particle filters have become popular. In this paper, we compare different localization methods using Kalman filtering, grid-based Markov localization, Monte Carlo Localization (MCL), and combinations thereof. We give experimental evidence that a combination of Markov localization and Kalman filtering as well as a variant of MCL outperform the other methods in terms of accuracy, robustness, and time needed for recovering from manual robot displacement, while requiring only few computational resources.

Rao-Blackwellised particle filtering for fault diagnosis

Abstract: We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as Rao-Blackwellised particle filtering. In this setting, there is one different linear-Gaussian state space model for each possible discrete state of operation. The task of diagnosis is to identify the discrete state of operation using the continuous measurements corrupted by Gaussian noise. The method is applied to the diagnosis of faults in planetary rovers.

A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods

Abstract: Recent Bayesian methods for the analysis of infectious disease outbreak data using stochastic epidemic models are reviewed. These methods rely on Markov chain Monte Carlo methods. Both temporal and non-temporal data are considered. The methods are illustrated with a number of examples featuring different models and datasets.

A Color-based Particle Filter

Abstract: Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has been proven very successful for non-linear and non-Gaussian estimation problems. However, for the tracking of non-rigid objects, the selection of reliable image features is also essential. This paper presents the integration of color distributions into particle filtering, which has typically used edge-based image features. Color distributions are applied as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. Thus, the target model of the particle filter is defined by the color information of the tracked object. As the tracker should find the most probable sample distribution, the model is compared with the current hypotheses of the particle filter using the Bhattacharyya coefficient, which is a popular similarity measure between two distributions. The proposed tracking method directly incorporates the scale and motion changes of the objects. Comparisons with the well known mean shift tracker show the advantages and limitations of the new approach. Keywords— Object tracking, Condensation algorithm, Color filtering, Bhattacharyya coefficient, Mean shift tracking

Particle methods for Bayesian modeling and enhancement of speech signals

Abstract: This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The main aim of the paper is to perform on-line estimation of the clean speech and model parameters and to determine the adequacy of the chosen statistical models. Efficient particle methods are developed to solve the optimal filtering and fixed-lag smoothing problems. The algorithms combine sequential importance sampling (SIS), a selection step and Markov chain Monte Carlo (MCMC) methods. They employ several variance reduction strategies to make the best use of the statistical structure of the model. It is also shown how model adequacy may be determined by combining the particle filter with frequentist methods. The modeling and enhancement performance of the models and estimation algorithms are evaluated in simulation studies on both synthetic and real speech data sets.

Markov chain Monte Carlo, Sufficient Statistics, and Particle Filters

Abstract: This article considers how to implement Markov chain Monte Carlo (MCMC) moves within a particle filter. Previous, similar, attempts have required the complete history (“trajectory”) of each particle to be stored. Here, it is shown how certain MCMC moves can be introduced within a particle filter when only summaries of each particles' trajectory are stored. These summaries are based on sufficient statistics. Using this idea, the storage requirement of the particle filter can be substantially reduced, and MCMC moves can be implemented more efficiently. We illustrate how this idea can be used for both the bearingsonly tracking problem and a model of stochastic volatility. We give a detailed comparison of the performance of different particle filters for the bearings-only tracking problem. MCMC, combined with a sensible initialization of the filter and stratified resampling, produces substantial gains in the efficiency of the particle filter.

Particle filtering for multi-target tracking and sensor management

Abstract: In this paper, we present computational methods based on particle filters to address the multi-target tracking and sensor management problems. We present a jump Markov model of multi-target systems and an efficient particle filtering algorithm to perform inference. In addition, we also present a formulation of the sensor management problem and its solution using particle methods.

Joint audio-visual tracking using particle filters

Abstract: It is often advantageous to track objects in a scene using multimodal information when such information is available. We use audio as a complementary modality to video data, which, in comparison to vision, can provide faster localization over a wider field of view. We present a particle-filter based tracking framework for performing multimodal sensor fusion for tracking people in a videoconferencing environment using multiple cameras and multiple microphone arrays. One advantage of our proposed tracker is its ability to seamlessly handle temporary absence of some measurements (e.g., camera occlusion or silence). Another advantage is the possibility of self-calibration of the joint system to compensate for imprecision in the knowledge of array or camera parameters by treating them as containing an unknown statistical component that can be determined using the particle filter framework during tracking. We implement the algorithm in the context of a videoconferencing and meeting recording system. The system also performs high-level semantic analysis of the scene by keeping participant tracks, recognizing turn-taking events and recording an annotated transcript of the meeting. Experimental results are presented. Our system operates in real time and is shown to be robust and reliable.