Abstract
Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms’ sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D body postures and visual fields, both in open and closed-loop contexts. Additionally, TRex offers highly-accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5-46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms and longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user interface.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Revised title and citations, as well as some URLs and figure images.
