TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields
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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 using background-subtraction with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, 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/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.
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WALTER, Tristan Leonard, Iain D. COUZIN, 2021. TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields. In: eLife. eLife Sciences Publications. 2021, 10, e64000. eISSN 2050-084X. Available under: doi: 10.7554/eLife.64000BibTex
@article{Walter2021multi-53221,
year={2021},
doi={10.7554/eLife.64000},
title={TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields},
volume={10},
journal={eLife},
author={Walter, Tristan Leonard and Couzin, Iain D.},
note={Article Number: e64000}
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