Cater: Combined Animal Tracking & Environment Reconstruction

Lars Haalck, Michael Mangan, Antoine Wystrach, Leo Clement, Barbara Webb, Benjamin Risse

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Quantifying the behavior of small animals traversing long distances in complex environments is one of the most difficult tracking scenarios for computer vision. Tiny and low-contrast foreground objects have to be localized in cluttered and dynamic scenes as well as trajectories compensated for camera motion and drift in multiple lengthy recordings. We introduce CATER, a novel methodology combining an unsupervised probabilistic detection mechanism with a globally optimized environment reconstruction pipeline enabling precision behavioral quantification in natural environments. Implemented as an easy to use and highly parallelized tool, we show its application to recover fine-scale motion trajectories, registered to a high-resolution image mosaic reconstruction, of naturally foraging desert ants from unconstrained field recordings. By bridging the gap between laboratory and field experiments, we gain previously unknown insights into ant navigation with respect to motivational states, previous experience, and current environments and provide an appearance-agnostic method applicable to study the behavior of a wide range of terrestrial species under realistic conditions.

Original languageEnglish
Article numbereadg2094
Pages (from-to)1-13
JournalScience Advances
Issue number16
Publication statusPublished - 21 Apr 2023


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