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TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields

View ORCID ProfileTristan Walter, View ORCID ProfileIain D Couzin
doi: https://doi.org/10.1101/2020.10.14.338996
Tristan Walter
1Max Planck Institute of Animal Behavior, Germany
2Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany
3Department of Biology, University of Konstanz, Germany
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  • For correspondence: twalter@ab.mpg.de icouzin@ab.mpg.de
Iain D Couzin
1Max Planck Institute of Animal Behavior, Germany
2Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany
3Department of Biology, University of Konstanz, Germany
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  • For correspondence: twalter@ab.mpg.de icouzin@ab.mpg.de
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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 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Revised document structure, reordered sections Results; Discussion; Materials&Methods Revised title and citations, as well as some URLs and figure images.

  • https://trex.run

  • a opencv.org

  • b tensorflow.org

  • c deeplearning.net

  • d The baslerweb.com Pylon SDK is required to be installed to support Basler USB cameras.

  • a See keras.io documentation for default arguments

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 23, 2021.
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TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields
Tristan Walter, Iain D Couzin
bioRxiv 2020.10.14.338996; doi: https://doi.org/10.1101/2020.10.14.338996
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TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields
Tristan Walter, Iain D Couzin
bioRxiv 2020.10.14.338996; doi: https://doi.org/10.1101/2020.10.14.338996

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