This library provides the julia-language implementation for VTTrac (https://github.com/thorinouchi/VTTrac). It does not use module variables, so it should be good for parallel execution.
The algorithm used in this library is the simple template matching of PIV (particle image velocimetry) for monochromatic image-like data, but the matching is conducted multiple times in a Lagrangian manner as in PTV (particle tracking velocimetry) over a number of times specified by the parameter named ntrac. The default scoring method for template matching is the cross correlation coefficient, as in the basic PIV. Both forward and backward tracking is available. Use the parameter itstep; tracking is backward along time sequence, if it is negative.
using Pkg
Pkg.add("VTTrac")Documenter.jl generated documentation:
You can contribute to this package by opening issues on GitHub or implementing things yourself and making a pull request. We'd also appreciate more example documents written using VTTrac.
VTTracby Takeshi Horinouchi: https://github.com/thorinouchi/VTTracpyVTTracby Taiga Tsukada: https://github.com/tsukada-cs/pyVTTrac
- Horinouchi, T., S. Tsujino, M. Hayashi, U. Shimada, W. Yanase, A. Wada, and H. Yamada, 2023: Stationary and Transient Asymmetric Features in Tropical Cyclone Eye with Wavenumber-1 Instability: Case Study for Typhoon Haishen (2020) with Atmospheric Motion Vectors from 30-Second Imaging. Monthly Weather Review, 151, 253–273, https://doi.org/10.1175/MWR-D-22-0179.1.
- Tsukada, T., T. Horinouchi, and S. Tsujino, 2024: Wind Distribution in the Eye of Tropical Cyclone Revealed by a Novel Atmospheric Motion Vector Derivation. JGR Atmospheres, 129, e2023JD040585, https://doi.org/10.1029/2023JD040585.