The Bin-Occupancy Filter and Its Connection to the PHD Filters

Erdinc, Ozgur; Willett, Peter; Bar‐Shalom, Yaakov

doi:10.1109/tsp.2009.2025816

Cited by 93 publications

(80 citation statements)

References 20 publications

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“…Moreover, Erdinc et al deduced the prediction and update equations by a "bin-occupancy" model in [108], which is connected to PHD/CPHD filtering. A method for deriving the PHD and CPHD recursions, without using FISST, is presented in [109,110].…”

Section: Multitarget Bayesian Filteringmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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Section: Multitarget Bayesian Filteringmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

View full text Add to dashboard Cite

show abstract

“…As is well-known [31], this problem is due to the Poisson assumption upon which the derivation of the update step of the PHD filter [7] relies, and is corrected by the CPHD filter [17].…”

Section: Remarkmentioning

confidence: 99%

Bayesian Multi-Object Filtering for Pairwise Markov Chains

Petetin

Desbouvries

2013

IEEE Trans. Signal Process.

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Abstract-Random Finite Sets (RFS) are recent tools for addressing the multi-object filtering problem. The Probability Hypothesis Density (PHD) Filter is an approximation of the multiobject Bayesian filter which results from the RFS formulation of the problem and has been used in many applications. In the RFS framework, it is assumed that each target and associated observation follow a Hidden Markov Chain (HMC) model. HMCs conveniently describe some physical properties of practical interest for practitioners, but they also implicitly imply restrictive independence properties which, in practice, may not be satisfied by data. In this paper, we show that these structural limitations of HMC models can somehow be relaxed by embedding them into the more general class of Pairwise Markov Chain (PMC) models. We thus focus on the computation of the PHD filter in a PMC framework, and we propose a practical implementation of the PHD filter for a particular class of PMC models.

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“…given all the measurements from time 1 to k. The bin-occupancy filter, which is described in [18], aims to estimate the probability of a target being in a given point. The approach is derived via a discretized state-space model of the surveillance region, where each grid cell (denoted bin in this approach) can or may not contain a target.…”

Section: Mappingmentioning

confidence: 99%

“…The approach is derived via a discretized state-space model of the surveillance region, where each grid cell (denoted bin in this approach) can or may not contain a target. One of the important assumptions in [18] is that the bins are sufficiently small so that each bin is occupied by maximum one target. In the limiting case, when the volume of the bins |ν| goes to zero, it is possible to define the bin-occupancy density…”

Section: Mappingmentioning

confidence: 99%

See 1 more Smart Citation

Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Lundquist

Hammarstrand

Gustafsson

2011

IEEE Trans. Signal Process.

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Abstract-Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into different clusters, which structures the description of the prior and considerably improves the measurement update in the PHD filter. Second, a merging step is proposed to simplify the map representation in the PHD filter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.

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The Bin-Occupancy Filter and Its Connection to the PHD Filters

Cited by 93 publications

References 20 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Bayesian Multi-Object Filtering for Pairwise Markov Chains

Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

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