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

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 quanti...

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Veröffentlicht in:	SN computer science 2021-11, Vol.2 (6), p.485, Article 485
Hauptverfasser:	Herzog, Sebastian, Schiepel, Daniel, Guido, Isabella, Barta, Robin, Wagner, Claus
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Automation Brownian motion Calibration Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Image reconstruction Information Systems and Communication Service Iterative methods Measurement techniques Methods Modelling Motion systems Open source software Original Research Particle tracking Particle tracking velocimetry Particle trajectories Pattern Recognition and Graphics Pipe flow Probabilistic models Rayleigh-Benard convection Software Engineering/Programming and Operating Systems Turbulent flow Velocity Vision
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