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CATER: Combined Animal Tracking & Environment Reconstruction.

Lars Haalck1, Michael Mangan2, Antoine Wystrach3

  • 1Institute for Geoinformatics and Institute for Computer Science, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany.

Science Advances
|April 21, 2023
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Summary

We developed CATER, a new computer vision method for tracking small animals in complex natural environments. This tool precisely quantifies animal behavior and navigation, offering new insights into foraging and movement ecology.

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Area of Science:

  • Computer Vision
  • Animal Behavior
  • Ecology

Background:

  • Tracking small animals in natural environments is challenging due to object size, low contrast, and cluttered scenes.
  • Existing methods struggle with camera motion, drift, and long recording durations.

Purpose of the Study:

  • To introduce CATER, a novel methodology for precise behavioral quantification of animals in natural settings.
  • To enable fine-scale motion trajectory recovery and environment reconstruction from unconstrained field recordings.

Main Methods:

  • CATER combines unsupervised probabilistic detection with a globally optimized environment reconstruction pipeline.
  • The method is implemented as an easy-to-use, highly parallelized tool.
  • Applied to track desert ants during natural foraging behavior.

Main Results:

  • Successfully recovered fine-scale motion trajectories of foraging desert ants.
  • Generated high-resolution image mosaic reconstructions of the ants' environment.
  • Provided previously unknown insights into ant navigation strategies.

Conclusions:

  • CATER bridges the gap between laboratory and field experiments for animal behavior studies.
  • The appearance-agnostic method is applicable to a wide range of terrestrial species.
  • Enables studying animal navigation in relation to motivation, experience, and environment.