Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in evolutionary and ecological studies.
| Version: | 0.4 |
| Imports: | png, SDMTools, neuralnet, raster, Rcpp, MASS, grDevices, graphics, stats, shiny |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2018-05-15 |
| Author: | Marjolein Bruijning, Marco D. Visser, Caspar A. Hallmann, Eelke Jongejans |
| Maintainer: | Marjolein Bruijning <m.bruijning at science.ru.nl> |
| BugReports: | https://github.com/marjoleinbruijning/trackdem/issues |
| License: | GPL-2 |
| URL: | https://github.com/marjoleinbruijning/trackdem |
| NeedsCompilation: | yes |
| SystemRequirements: | Python 2.7, Libav, ExifTool |
| CRAN checks: | trackdem results |
| Reference manual: | trackdem.pdf |
| Vignettes: |
Tutorial |
| Package source: | trackdem_0.4.tar.gz |
| Windows binaries: | r-devel: trackdem_0.4.zip, r-release: trackdem_0.4.zip, r-oldrel: trackdem_0.4.zip |
| OS X binaries: | r-release: trackdem_0.4.tgz, r-oldrel: trackdem_0.3.1.tgz |
| Old sources: | trackdem archive |
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