MHT-X: offline multiple hypothesis tracking with algorithm X

An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans wi...

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Veröffentlicht in:Experiments in fluids Jg. 63; H. 3
Hauptverfasser: Zvejnieks, Peteris, Birjukovs, Mihails, Klevs, Martins, Akashi, Megumi, Eckert, Sven, Jakovics, Andris
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Springer Nature B.V
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ISSN:0723-4864, 1432-1114
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Abstract An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for edge construction for maximum likelihood trajectories. The current version of the code was developed for applications in multi-phase hydrodynamics, e.g., bubble and particle tracking, and is capable of resolving object motion, merges and splits. Feasible object associations and trajectory graph edge likelihoods are determined using weak mass and momentum conservation laws translated to statistical functions for object properties. The code is compatible with n-dimensional motion with arbitrarily many tracked object properties. This framework is easily extendable beyond the present application by replacing the currently used heuristics with ones more appropriate for the problem at hand. The code is open-source and will be continuously developed further. Graphical abstract
AbstractList An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for edge construction for maximum likelihood trajectories. The current version of the code was developed for applications in multi-phase hydrodynamics, e.g., bubble and particle tracking, and is capable of resolving object motion, merges and splits. Feasible object associations and trajectory graph edge likelihoods are determined using weak mass and momentum conservation laws translated to statistical functions for object properties. The code is compatible with n-dimensional motion with arbitrarily many tracked object properties. This framework is easily extendable beyond the present application by replacing the currently used heuristics with ones more appropriate for the problem at hand. The code is open-source and will be continuously developed further.
An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for edge construction for maximum likelihood trajectories. The current version of the code was developed for applications in multi-phase hydrodynamics, e.g., bubble and particle tracking, and is capable of resolving object motion, merges and splits. Feasible object associations and trajectory graph edge likelihoods are determined using weak mass and momentum conservation laws translated to statistical functions for object properties. The code is compatible with n-dimensional motion with arbitrarily many tracked object properties. This framework is easily extendable beyond the present application by replacing the currently used heuristics with ones more appropriate for the problem at hand. The code is open-source and will be continuously developed further. Graphical abstract
ArticleNumber 55
Author Zvejnieks, Peteris
Klevs, Martins
Birjukovs, Mihails
Akashi, Megumi
Eckert, Sven
Jakovics, Andris
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  givenname: Peteris
  surname: Zvejnieks
  fullname: Zvejnieks, Peteris
  email: peteris.zvejnieks@lu.lv
  organization: Institute of Numerical Modelling, University of Latvia (UL)
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  givenname: Mihails
  orcidid: 0000-0002-4753-737X
  surname: Birjukovs
  fullname: Birjukovs, Mihails
  email: mihails.birjukovs@lu.lv
  organization: Institute of Numerical Modelling, University of Latvia (UL)
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  givenname: Martins
  surname: Klevs
  fullname: Klevs, Martins
  organization: Institute of Numerical Modelling, University of Latvia (UL)
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  givenname: Megumi
  surname: Akashi
  fullname: Akashi, Megumi
  organization: Department of Magnetohydrodynamics, Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
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  givenname: Sven
  surname: Eckert
  fullname: Eckert, Sven
  organization: Department of Magnetohydrodynamics, Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
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  givenname: Andris
  surname: Jakovics
  fullname: Jakovics, Andris
  organization: Institute of Numerical Modelling, University of Latvia (UL)
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Snippet An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python....
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SubjectTerms Algorithms
Conservation laws
Engineering
Engineering Fluid Dynamics
Engineering Thermodynamics
Fluid- and Aerodynamics
Graph theory
Heat and Mass Transfer
Hypotheses
Object motion
Particle tracking
Research Article
Source code
Windows (intervals)
Title MHT-X: offline multiple hypothesis tracking with algorithm X
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https://www.proquest.com/docview/2637578453
Volume 63
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