Particle Filters for Multiple Target Tracking (original) (raw)
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Monte Carlo filtering for multi-target tracking and data association
IEEE Transactions on Aerospace and Electronic Systems, 2005
We present Monte Carlo methods for multi-target tracking and data association. The methods are applicable to general nonlinear and non-Gaussian models for the target dynamics and measurement likelihood. We provide efficient solutions to two very pertinent problems: the data association problem that arises due to unlabelled measurements in the presence of clutter, and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets. We develop a number of algorithms to achieve this. The first, which we refer to as the Monte Carlo joint probabilistic data association filter (MC-JPDAF), is a generalisation of the strategy proposed in [1] and [2]. As is the case for the JPDAF, the distributions of interest are the marginal filtering distributions for each of the targets, but these are approximated with particles rather than Gaussians. We also develop two extensions to the standard particle filtering methodology for tracking multiple targets. The first, which we refer to as the sequential sampling particle filter (SSPF), samples the individual targets sequentially by utilising a factorisation of the importance weights. The second, which we refer to as the independent partition particle filter (IPPF), assumes the associations to be independent over the individual targets, leading to an efficient component-wise sampling strategy to construct new particles. We evaluate and compare the proposed methods on a challenging synthetic tracking problem.
International Journal of Artificial Intelligence & Applications, 2012
Joint multiple target tracking and classification is an important issue in many engineering applications. In recent years, multiple sensor data fusion has been extensively investigated by researchers in a variety of disciplines. Indeed, combining results issued from multiple sensors can provide more accurate information than using a single sensor. In the present paper we address the problem of data fusion for joint multiple maneuvering target tracking and classification in cluttered environment where centralized versus decentralized architectures are often opposed. The proposal advocates a hybrid approach combining a Particle Filter (PF) like method to deal with system nonlinearities and Fitgerald's Cheap Joint Probabilistic Data Association Filter CJPDAF for the purpose of data association and target estimation problems, yielding CJPDA-PF algorithm. While the target maneuverability is tackled using a combination of a Multiple Model Filter (MMF) and CJPDAF, which yields CJPDA-MMPF algorithm. Consequently, at each particle level (of the particle filter), the state of the particle is evaluated using the suggested CJPDA-MMF. In case of several sensors, the centralized fusion architecture and the distributed architecture in the sense of Federated Kalman Filtring are investigated and compared. The feasibility and the performances of the proposal have been demonstrated using a set of Monte Carlo simulations dealing with two maneuvering targets with two distinct operation modes and various clutter densities.
International Journal of Advanced Computer Science and Applications, 2011
Improving data association technique in dense clutter environment for multi-target tracking used in Markov chain Monte Carlo based particle filter (MCMC-PF) are discussed in this paper. A new method named Viterbi filtered gate Markov chain Monte Carlo VFG-MCMC is introduced to avoid track swap and to overcome the issue of losing track to highly maneuvering targets in the presence of more background clutter and false signals. An adaptive search based on Viterbi algorithm is then used to detect the valid filtered data point in each target gate. The detected valid point for each target is applied to the estimation algorithm of MCMC-PF during calculating the sampling weights. This proposed method makes the MCMC interacts only with the valid target that is candidate from the filtered gate and no more calculations are considered for invalid targets. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm MCMC-PF.
In this paper we propose to use Regularized Monte Carlo-Joint Probabilistic Data Association Filter (RMC-JPDAF) to the classical problem of multiple target tracking in a cluttered area. We have used the Monte Carlo methods in order to the fact that they have the ability to model any state-space with nonlinear and non-Gaussian models for target dynamics and measurement likelihood. To encounter with the data association problem that arises due to unlabeled measurements in the presence of clutter, we have used the Joint Probabilistic Data Association Filter (JPDAF). Due to the resampling stage in the MC-JPDAF, the sample impoverishment phenomenon is unavoidable and the tracking performance will decrease. So we propose to use the Regularized resampling stage instead, to counteract this effect. Finally we have used the target dynamics model as the proposal distribution in MC-JPDAF, in order to decrease the computational cost while the performance of the tracking system is nearly maintained.
Multiple target tracking using Sequential Monte Carlo Methods and statistical data association
International Conference on Intelligent RObots and Systems - IROS, 2003
This paper presents two approaches for the problem of Multiple Target Tracking (MTT) and specifically people tracking. Both filters are based on Sequential Monte Carlo Methods (SMCM) and Joint Probability Data Association (JPDA). The filters have been implemented and tested on real data from a laser measurement system. Experiments show that both approaches are able to track multiple moving persons. A comparison of both filters is given and the advantages and disadvantages of the two approaches are presented.
Multi-target tracking in clutter with sequential Monte Carlo methods
IET Radar, Sonar & Navigation, 2010
For multi-target tracking (MTT) in the presence of clutters, both issues of state estimation and data association are crucial. This study tackles them jointly by Sequential Monte Carlo methods, a.k.a. particle filters. A number of novel particle algorithms are devised. The first one, which we term Monte-Carlo data association (MCDA), is a direct extension of the classical sequential importance resampling (SIR) algorithm. The second one is called maximum predictive particle filter (MPPF), in which the measurement combination with the maximum predictive likelihood is used to update the estimate of the multi-target's posterior. The third, called proportionally weighting particle filter (PWPF), weights all feasible measurement combinations according to their predictive likelihoods, and uses them proportionally in the importance sampling framework. We demonstrate the efficiency and superiority of our methods over conventional approaches through simulations. This paper tackles data association jointly with state estimation via Sequential Monte Carlo (SMC) methods, a.k.a. particle filters (PF) [9-11]. Such particle methods
Joint particle filtering of multiple maneuvering targets from unassociated measurements
Journal of Advances in Information Fusion, 2006
The problem of maintaining tracks of multiple maneuvering targets from unassociated measurements is formulated as a problem of estimating the hybrid state of a Markov jump linear system from measurements made by a descriptor system with independent, identically distributed (i.i.d.) stochastic coefficients. This characterization is exploited to derive the exact equation for the Bayesian recursive filter, to develop two novel Sampling Importance Resampling (SIR) type particle filters, and to derive approximate Bayesian filters which use for each target one Gaussian per maneuver mode. The two approximate Bayesian filters are a compact and a trackcoalescence avoiding version of Interacting Multiple Model Joint Probabilistic Data Association (IMMJPDA). The relation of each of the four novel filter algorithms to the literature is well explained. Through Monte Carlo simulations for a two target example, these four filters are compared to each other and to the approach of using one IMMPDA filter per target track. The Monte Carlo simulation results show that each of the four novel filters clearly outperforms the IMMPDA approach. The results also show under which conditions the IMMJPDA type filters perform close to exact Bayesian filtering, and under which conditions not.
Data Association for an Adaptive Multi-target Particle Filter Tracking System
This paper presents a novel approach to improve the accuracy of tracking multiple objects in a static scene using a particle filter system by introducing a data association step, a state queue for the collection of tracked objects and adaptive parameters to the system. The data association step makes use of the object detection phase and appearance model to determine if the approximated targets given by the particle filter step match the given set of detected objects. The remaining detected objects are used as information to instantiate new objects for tracking. State queues are also used for each tracked object to deal with occlusion events and occlusion recovery. Finally we present how the parameters adjust to occlusion events. The adaptive property of the system is also used for possible occlusion recovery. Results of the system are then compared to a ground truth data set for performance evaluation. Our system produced accurate results and was able to handle partially occluded objects as well as proper occlusion recovery from tracking multiple objects.
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
Sensors (Basel, Switzerland), 2017
We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tra...