PMHT: problems and some solutions

Willett, Peter; Ruan, Yanhua; Streit, Roy L.

doi:10.1109/taes.2002.1039396

Cited by 157 publications

(107 citation statements)

References 9 publications

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“…Another method for handling data association in the Bayesian manner is the probabilistic multihypothesis tracker (PMHT) [58,59]. PMHT iteratively computes data association probabilities and track updates, using the expectation maximization (EM) method.…”

Section: Bayesian Approachesmentioning

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: Bayesian Approachesmentioning

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 traditional approaches to multiple hypothesis tracking rely on the complete enumeration of all possible association interpretations of a series of measurements and avoid an exponential growth of the arising hypothesis trees by various approximations (MHT: Multiple Hypothesis Tracking [22], [23], (J)PDAF: (Joint) Probabilistic Data Association Filter [20]). A powerful, alternative approach is represented by Probabilistic Multiple Hypothesis Tracking (PMHT) [25], [27], [28], [31]. Essentially PMHT is based on Expectation-Maximization for handling assignment conflicts.…”

Section: Introductionmentioning

confidence: 99%

A Security Assistance System Combining Person Tracking with Chemical Attributes and Video Event Analysis

Becher

Foresti

Kaul

et al. 2009

Forschungsspitzen Und Spitzenforschung

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“…The PMHT offers an attractive alternative to more traditional tracking approaches because it easily accommodates model complexity (such as multiple target dynamics models for manoeuvring targets) [4][5][6], and because its computation requirements scale linearly with the problem size [3].…”

Section: Introductionmentioning

confidence: 99%

“…The standard PMHT restricts the prior to be either constant, or time independent. Existing PMHT research does not address the assignment prior; effort has instead focussed on the target dynamics model [4,8,6,9], more sophisticated measurement models [10][11][12], and matters of practical significance for realistic implementations [7,13,14]. A model is proposed here which allows the assignment prior to be a randomly evolving quantity.…”

Section: Introductionmentioning

confidence: 99%