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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Vydáno v:Experiments in fluids Ročník 63; číslo 3
Hlavní autoři: Zvejnieks, Peteris, Birjukovs, Mihails, Klevs, Martins, Akashi, Megumi, Eckert, Sven, Jakovics, Andris
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Springer Nature B.V
Témata:
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)
ISSN:0723-4864, 1432-1114
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Shrnutí: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
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0723-4864
1432-1114
DOI:10.1007/s00348-022-03399-5