PT  - JOURNAL ARTICLE
AU  - Tristan Walter
AU  - Iain D Couzin
TI  - TRex, a fast multi-animal tracking system with markerless identification, 2D body posture estimation and visual field reconstruction
AID  - 10.1101/2020.10.14.338996
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.14.338996
4099  - http://biorxiv.org/content/early/2020/10/15/2020.10.14.338996.short
4100  - http://biorxiv.org/content/early/2020/10/15/2020.10.14.338996.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.