A Bayesian approach to extended object tracking and tracking of loosely structured target groups

Koch, Wolfgang; Saul, Roman

doi:10.1109/icif.2005.1591939

Cited by 21 publications

(12 citation statements)

References 6 publications

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“…1) Sensor model: The aim of the sensor model is to describe the statistical behavior of the measurements, given z k . The behavior of the clutter detections is readily given by (7), but modelling the target detections (8) -(9) is more complicated. It is crucial that these models capture the behavior of the vehicle detections from different aspect angles and at all ranges [21].…”

Section: Objectivesmentioning

confidence: 99%

“…A good overview of different contributions up to 2004 can be found in [6], covering extended object tracking and the closely related problem of tracking groups of targets. More recent suggestions include, [7], [8] where a formal Bayesian tracking framework is proposed for estimating the centroid of the extended targets (or target groups). The object extension is modelled as an ellipse and it is assumed that multiple measurements can originate from each object.…”

Section: Introductionmentioning

confidence: 99%

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Extended Object Tracking using a Radar Resolution Model

Hammarstrand

Svensson

Sandblom

et al. 2012

IEEE Trans. Aerosp. Electron. Syst.

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This paper concerns the problem of vehicle tracking when multiple radar reflection centers could be resolved on each vehicle. For this extended target tracking problem we propose a radar sensor model, capable of describing such measurements, incorporating sensor resolution. Furthermore, we introduce approximations to handle the inherently complex data association problem. The evaluation in terms of describing measured data and resulting tracking performance shows that the model effectively exploits the information in multiple vehicle detections.

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Section: Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended Object Tracking using a Radar Resolution Model

Hammarstrand

Svensson

Sandblom

et al. 2012

IEEE Trans. Aerosp. Electron. Syst.

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“…Various methods have been developed: i) for a relatively small number of groups, with a small number of group components [1,2,3,4,5], and ii) for groups comprised of hundreds or thousands of objects [6,7]. In the second case the whole group is usually considered as an extended object (an ellipse or a circle) which centre position is estimated, together with the parameters of the extent.…”

Section: Introductionmentioning

confidence: 99%

Particle algorithms for filtering in high dimensional state spaces: A case study in group object tracking

Mihaylova

Carmi

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.

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“…Usually the problem is formulated as a joint estimation of kinematic states and parameters, where the parameters relate to the extent of the object of interest [1], [2], [3], [4], [5], [6], [7] and the main methodology is the Bayesian framework. Various filters have been developed for extended target tracking: particle track-before-detect filters [8], cluster based approaches [9], [10], Poisson spatial models combined with particle filters (PFs) [11], [12], [13], [14] and mixture Kalman filters combined with data augmentation [1].…”

Section: Introductionmentioning

confidence: 99%

On the performance of the Box Particle Filter for extended object tracking using laser data

Petrov

Ulmke

Mihaylova

et al. 2012

2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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This paper considers the challenging task of realtime extended object tracking using cluttered measurements from laser range scanners. The performance of the recently proposed Box Particle Filter (Box PF) algorithm is evaluated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor.The problem is expressed as the joint estimation of both state and parameters of an extended target. Circularly and elliptically shaped targets are considered. Promising results are presented.

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A Bayesian approach to extended object tracking and tracking of loosely structured target groups

Cited by 21 publications

References 6 publications

Extended Object Tracking using a Radar Resolution Model

Extended Object Tracking using a Radar Resolution Model

Particle algorithms for filtering in high dimensional state spaces: A case study in group object tracking

On the performance of the Box Particle Filter for extended object tracking using laser data

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