Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.14.338996v1?rss=1 Authors: Walter, T., Couzin, I. D. 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 visualization and data-exploration within an intuitive, platform-independent graphical user interface. Copy rights belong to original authors. Visit the link for more info