Hybrid Algorithms for Multitarget Tracking using MHT and GM-CPHD

Pollard, Evangeline; Pannetier, B.; Rombaut, Michèle

doi:10.1109/taes.2011.5751229

Cited by 27 publications

(14 citation statements)

References 33 publications

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“…Then the peaks are association to target tracks by traditional data association methods, such as PHD/CPHD filtering with MHT [146,147], PHD/CPHD filtering with the connectivity graph and cross entropy (CE) technique [148], CPHD filtering with fuzzy logy [149], 2-D assignment for PHD filtering [150,151] and CPHD filtering [152,153], and PHD/CPHD filtering with graph matching [118,140].…”

Section: ) Target Birth and Spawning Modelmentioning

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: ) Target Birth and Spawning Modelmentioning

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

“…The 1] is used to spread the previous approximate posteriors. Each birth component and spawned component is assigned a new label, and the complete predicted label thus has the form…”

Section: Analytic Implementation Of the Extended Phd Filtermentioning

confidence: 99%

“…In recent years, the development of the probability hypothesis density (PHD) filter as an approach to multi-target tracking (MTT) have attracted substantial interest [1][2][3]. The PHD filters operate on the single-target state space and avoid the problem that arises from data association, which have been shown to be a computationally tractable method for unified group target detection [2], tracking [3,4] and classification [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive noise variance identification for probability hypothesis density‐based multi‐target filter by variational Bayesian approximations

Huang

Gao

2013

IET Radar, Sonar & Navigation

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A new extended probability hypothesis density (PHD) filter is proposed for joint estimation of the time-varying number of targets and their states without the measurement noise variance. The extended PHD filter can adaptively learn the unknown noise parameters at each scan time by using the received measurements. With the decomposition of the posterior intensity separated into Gaussian and Inverse-Gamma components, the closed-form solutions to the extended PHD filter are derived by using the variational Bayesian approximations, which have been proved as a simple, analytically tractable method to approximate the posterior intensity of multi-target states and time-varying noise variances. Simulation results show that the proposed filter can accommodate the unknown measurement variances effectively, and improve the estimation accuracy of both the number of targets and their states.

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“…Pollard et al developed a hybrid combination of the multiple hypothesis tracking (MHT) filter and the Gaussian mixture CPHD (GM-CPHD) filter. 17 The GM-CPHD filter provides a robust cardinality estimate of the multitarget state which is complemented by accurate state estimates from the MHT filter. Panta et al proposed a similar hybrid approach combining the MHT and PHD filters; 18 in their work the PHD filter is used as a clutter filter to gate the input for the MHT filter.…”

Section: Introductionmentioning

confidence: 99%

Hybrid multi-Bernoulli CPHD filter for superpositional sensors

Nannuru

Coates

2014

SPIE Proceedings

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We propose, for the superpositional sensor scenario, a hybrid between the multi-Bernoulli filter and the cardinalized probability hypothesis density (CPHD) filter. We use a multi-Bernoulli random finite set (RFS) to model existing targets and we use an independent and identically distributed cluster (IIDC) RFS to model newborn targets and targets with low probability of existence. Our main contributions are providing the update equations of the hybrid filter and identifying computationally tractable approximations. We achieve this by defining conditional probability hypothesis densities (PHDs), where the conditioning is on one of the targets having a specified state. The filter performs an approximate Bayes update of the conditional PHDs. In parallel, we perform a cardinality update of the IIDC RFS component in order to estimate the number of newborn targets. We provide an auxiliary particle filter based implementation of the proposed filter and compare it with CPHD and multi-Bernoulli filters in a simulated multitarget tracking application.

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Hybrid Algorithms for Multitarget Tracking using MHT and GM-CPHD

Cited by 27 publications

References 33 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Adaptive noise variance identification for probability hypothesis density‐based multi‐target filter by variational Bayesian approximations

Hybrid multi-Bernoulli CPHD filter for superpositional sensors

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