MHT-X: Offline Multiple Hypothesis Tracking with Algorithm X
| Autor: | Peteris Zvejnieks, Mihails Birjukovs, Martins Klevs, Megumi Akashi, Sven Eckert, Andris Jakovics |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Fluid Flow and Transfer Processes
FOS: Computer and information sciences bubble dynamics Computer Vision and Pattern Recognition (cs.CV) neutron imaging Computational Mechanics Computer Science - Computer Vision and Pattern Recognition Fluid Dynamics (physics.flu-dyn) General Physics and Astronomy FOS: Physical sciences Physics - Fluid Dynamics Algorithm X image processing two-phase flow Mechanics of Materials liquid metal X-ray radiography |
| Zdroj: | arXiv:2101.05202 [cs.CV]: https://arxiv.org/abs/2101.05202 Experiments in Fluids 63(2022)3, 55 |
| Popis: | 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 multiphase 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. Comment: 18 pages, 15 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|