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A Probabilistic Particle Tracking Framework for Guided and Brownian Motion Systems with High Particle Densities

Herzog, Sebastian and Schiepel, Daniel and Guido, Isabella and Barta, Robin and Wagner, Claus (2021) A Probabilistic Particle Tracking Framework for Guided and Brownian Motion Systems with High Particle Densities. SN Computer Science, 2 (485), pp. 1-20. Springer Nature. doi: 10.1007/s42979-021-00879-z. ISSN 2661-8907.

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Official URL: https://link.springer.com/article/10.1007%2Fs42979-021-00879-z


This paper presents a new framework for particle tracking based on a Gaussian Mixture Model (GMM). It is an extension of the state-of-the-art iterative reconstruction of individual particles by a continuous modeling of the particle trajectories considering the position and velocity as coupled quantities. The proposed approach includes an initialization and a processing step. In the first step, the velocities at the initial points are determined after iterative reconstruction of individual particles of the first four images to be able to generate the tracks between these initial points. From there on, the tracks are extended in the processing step by searching for and including new points obtained from consecutive images based on continuous modeling of the particle trajectories with a Gaussian Mixture Model. The presented tracking procedure allows to extend existing trajectories interactively with low computing effort and to store them in a compact representation using little memory space. To demonstrate the performance and the functionality of this new particle tracking approach, it is successfully applied to a synthetic turbulent pipe flow, to the problem of observing particles corresponding to a Brownian motion (e.g., motion of cells), as well as to problems where the motion is guided by boundary forces, e.g., in the case of particle tracking velocimetry of turbulent Rayleigh-Bénard convection.

Item URL in elib:https://elib.dlr.de/145086/
Document Type:Article
Title:A Probabilistic Particle Tracking Framework for Guided and Brownian Motion Systems with High Particle Densities
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Herzog, SebastianMPI Dynamik, Göttingenhttps://orcid.org/0000-0001-7167-3489UNSPECIFIED
Schiepel, DanielUNSPECIFIEDhttps://orcid.org/0000-0002-3703-3514UNSPECIFIED
Date:1 November 2021
Journal or Publication Title:SN Computer Science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-20
EditorsEmailEditor's ORCID iDORCID Put Code
Publisher:Springer Nature
Keywords:Gaussian Mixture Model, Rayleigh–Bénard convection, Particle Tracking Velocimetry
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Rail Transport
DLR - Research area:Transport
DLR - Program:V SC Schienenverkehr
DLR - Research theme (Project):V - NGT BIT (old)
Location: Göttingen
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > Ground Vehicles
Deposited By: Schiepel, Dr. Daniel
Deposited On:05 Nov 2021 12:10
Last Modified:28 Jun 2023 13:09

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