ArticleScience advances2023
CATER: Combined Animal Tracking & Environment Reconstruction.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Updating an allocentric goal from lateralised egocentric visual memories.Nature communications · 2026Article
- Cognitive embodied learning for anomaly active target tracking.Communications engineering · 2025Article
- Route-centric ant-inspired memories enable panoramic route-following in a car-like robot.Nature communications · 2025Article
- Ant visual route navigation: How the fine details of behaviour promote successful route performance and convergence.PLoS computational biology · 2025Article
- vmTracking enables highly accurate multi-animal pose tracking in crowded environments.PLoS biology · 2025Article
- WildDrone: autonomous drone technology for monitoring wildlife populations.Frontiers in robotics and AI · 2025Article
- Compensation to visual impairments and behavioral plasticity in navigating ants.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Trail using ants follow idiosyncratic routes in complex landscapes.Learning & behavior · 2024Article
- Investigating visual navigation using spiking neural network models of the insect mushroom bodies.Frontiers in physiology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.