First-moment filters for spatial independent cluster processes

Swain, Anthony; Clark, Dan E.

doi:10.1117/12.850034

Cited by 18 publications

(6 citation statements)

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“…This form confirms the status of a multi-object likelihood for L c t . Interestingly, the structure of the joint multi-object tracking and camera calibration is similar to the one derived for group tracking, see, e.g., [42] and [43] . This similarity can be explained by the hierarchical structure shared by the two estimation problems.…”

Section: B Conditional Phd Filteringmentioning

confidence: 71%

“…We propose to address this problem as a doubly-stochastic inference problem [43], where the measurements are conditioned on the multiple-object locations, that are in turn conditioned on the relative camera orientations. A similar method using Random Finite Sets has been developed for the related problem of Simultaneous Localisation and Mapping (SLAM) for autonomous robot navigation [34], [27], [28], where each object measurement contributes both to a feature in the world and self-localisation of the vehicle.…”

Section: E Camera Calibrationmentioning

confidence: 99%

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A Unified Approach for Multi-Object Triangulation, Tracking and Camera Calibration

Houssineau

Clark

Iveković³

et al. 2016

IEEE Trans. Signal Process.

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Object triangulation, 3-D object tracking, feature correspondence, and camera calibration are key problems for estimation from camera networks. This paper addresses these problems within a unified Bayesian framework for joint multiobject tracking and sensor registration. Given that using standard filtering approaches for state estimation from cameras is problematic, an alternative parametrisation is exploited, called disparity space. The disparity space-based approach for triangulation and object tracking is shown to be more effective than non-linear versions of the Kalman filter and particle filtering for non-rectified cameras. The approach for feature correspondence is based on the Probability Hypothesis Density (PHD) filter, and hence inherits the ability to update without explicit measurement association, to initiate new targets, and to discriminate between target and clutter. The PHD filtering approach then forms the basis of a camera calibration method from static or moving objects. Results are shown on simulated data.

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Section: B Conditional Phd Filteringmentioning

confidence: 71%

Section: E Camera Calibrationmentioning

confidence: 99%

A Unified Approach for Multi-Object Triangulation, Tracking and Camera Calibration

Houssineau

Clark

Iveković³

et al. 2016

IEEE Trans. Signal Process.

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“…linear/Gaussian target tracking), then one can effectively apply a Rao-Blackwellised formulation. This is the essence of the algorithms proposed for: tracking groups of targets [57], tracking an extended target [58] and simultaneous localisation and mapping (SLAM) [44,46,59].…”

Section: Calibration Of Tracking Algorithmsmentioning

confidence: 99%

“…The GLMB is a very general and flexible RFS model, but its application to multi-target tracking problems is more easily demonstrated by considering a more specific type of GLMB RFS, which was also proposed in [64]. A δgeneralised labelled multi-Bernoulli (δ-GLMB) RFS with state space X and label space L, is a GLMB RFS with density of the same form as (57), with the following substitutions…”

Section: Labelled Rfsmentioning

confidence: 99%

An overview of particle methods for random finite set models

2016

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This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application.

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“…The single-cluster PHD filter was motivated by work on multi-group multi-target tracking [36] and extended object tracking [37]. More general models for processes with interactions extend these models [38], though in this paper we concentrate on the single-cluster PHD filter for modeling the SLAM process.…”

Section: Introductionmentioning

confidence: 99%

SLAM With Dynamic Targets via Single-Cluster PHD Filtering

Lee

Clark

Salví

2013

IEEE J. Sel. Top. Signal Process.

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Abstract-This paper presents the first algorithm for simultaneous localization and mapping (SLAM) that can estimate the locations of both dynamic and static features in addition to the vehicle trajectory. We model the feature-based SLAM problem as a singlecluster process, where the vehicle motion defines the parent, and the map features define the daughter. Based on this assumption, we obtain tractable formulae that define a Bayesian filter recursion. The novelty in this filter is that it provides a robust multi-object likelihood which is easily understood in the context of our starting assumptions. We present a particle/Gaussian mixture implementation of the filter, taking into consideration the challenges that SLAM presents over target tracking with stationary sensors, such as changing fields of view and a mixture of static and dynamic map features. Monte Carlo simulation results are given which demonstrate the filter's effectiveness with high measurement clutter and non-linear vehicle motion.

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First-moment filters for spatial independent cluster processes

Cited by 18 publications

References 0 publications

A Unified Approach for Multi-Object Triangulation, Tracking and Camera Calibration

A Unified Approach for Multi-Object Triangulation, Tracking and Camera Calibration

An overview of particle methods for random finite set models

SLAM With Dynamic Targets via Single-Cluster PHD Filtering

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