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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
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.
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.
