Particle Tracking and Demography

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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.5.2 by Marjolein Bruijning, a year ago

Report a bug at

Browse source code at

Authors: Marjolein Bruijning , Marco D. Visser , Caspar A. Hallmann , Eelke Jongejans

Documentation:   PDF Manual  

Task views: Handling and Analyzing Spatio-Temporal Data, Processing and Analysis of Tracking Data

GPL-2 license

Imports png, neuralnet, raster, Rcpp, MASS, grDevices, graphics, stats, shiny

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo

System requirements: Python (>=2.7), Libav, ExifTool

See at CRAN