Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines

Daniyan, Abdullahi; Lambotharan, Sangarapillai; Deligiannis, Anastasios; Gong, Yu; Chen, Wenhua

doi:10.1109/tsp.2018.2873537

Cited by 37 publications

(17 citation statements)

References 34 publications

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“…The extended object δ-GLMB and LMB filters have been used in several different applications: tracking cars under a rectangular extent assumption using LIDAR and RADAR [86], [162]- [164]; tracking of irregularly shaped objects using Gaussian processes or splines [165], [166]; and tracking in very high clutter levels in [167].…”

Section: Methodsmentioning

confidence: 99%

A Tutorial on Multiple Extended Object Tracking

Granström¹,

Baum²

2022

Preprint

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<div>This tutorial introduces state-of-the-art methods for tracking multiple spatially extended objects based on unlabeled noisy point clouds, e.g., from radar or lidar sensors. In the first part, the focus lies on tracking a single extended object, i.e., the objective is to simultaneously estimate the shape and position of a moving object based on spatially distributed noisy detections. Model-based approaches for tracking elliptical and star-convex shaped objects are treated. Furthermore, recent learning-based approaches are discussed, which learn the spatial distribution of detections from real data. The second part considers the track management and data association problem, i.e., the initialization and termination of tracks as well as the association of detections to objects. After an in-depth analysis of the data association problem for extended objects, various data association approaches are discussed. Finally, recent frameworks for multiple extended object tracking are introduced such as the random finite set-based Poisson multi-Bernoulli mixture framework.</div>

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Section: Methodsmentioning

confidence: 99%

A Tutorial on Multiple Extended Object Tracking

Granström¹,

Baum²

2022

Preprint

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“…B-Splines are a compact representation for a wide variety of shapes and they are, for instance, used for road estimation [16], [17]. Additionally, there exist spline-based methods for tracking extended targets [18], [19], [20]. [20] is the closest method to ours.…”

Section: Related Workmentioning

confidence: 99%

Fast 3D Extended Target Tracking using NURBS Surfaces

Naujoks

Burger

Wuensche

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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This paper proposes fast and novel methods to jointly estimate the target's unknown 3D shape and dynamics. Measurements are noisy and sparsely distributed 3D points from a light detection and ranging (LiDAR) sensor. The methods utilize non-uniform rational B-splines (NURBS) surfaces to approximate the target's shape. One method estimates Cartesian scaling parameters of a NURBS surface, whereas the second method estimates the corresponding NURBS weights, too. Major advantages are the capability of estimating a fully 3D shape as well as the fast processing time. Real-world evaluations with a static and dynamic vehicle show promising results compared to state-of-the-art 3D extended target tracking algorithms.

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“…For estimating object trajectories, MOT methods based on random vectors link an object state estimate with a previous estimate or declare the appearance of a new object. For RFSsbased MOT methods, one approach to estimating trajectories is to add a unique label to each single-object state such that each object can be identified over time [28], [31]- [33]. This track labelling procedure may work well in some cases, but it often becomes problematic in challenging scenarios [41]- [43].…”

Section: Introductionmentioning

confidence: 99%

Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation

Xia¹,

García-Fernández²,

Meyer³

et al. 2022

Preprint

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In this paper, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior after the update step is approximated as a PMB. The efficient PMB approximation relies on several important theoretical contributions. First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements. The PMBM density is a conjugate prior in the sense that both the prediction and the update steps preserve the PMBM form of the density. Second, we present a factor graph representation of the joint posterior of the PMBM set of trajectories and association variables for the Poisson spatial measurement model. Importantly, leveraging the PMBM conjugacy and the factor graph formulation enables an elegant treatment on undetected objects via a Poisson point process and efficient inference on sets of trajectories using BP, where the approximate marginal densities in the PMB approximation can be obtained without enumeration of different data association hypotheses. To achieve this, we present a particle-based implementation of the proposed filter, where smoothed trajectory estimates, if desired, can be obtained via single-object particle smoothing methods, and its performance for EOT with ellipsoidal shapes is evaluated in a simulation study.

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Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines

Cited by 37 publications

References 34 publications

A Tutorial on Multiple Extended Object Tracking

A Tutorial on Multiple Extended Object Tracking

Fast 3D Extended Target Tracking using NURBS Surfaces

Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation

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