Zero-Shot Multi-Animal Tracking in the Wild

Jan Frederik Meier, Timo Lüddecke
11/4/2025
cs.CV

Official code: ecker-lab/SAM2-Animal-Tracking (20 stars, last commit 2025-11-20).

Abstract

Multi-animal tracking is crucial for understanding animal ecology and behavior. However, it remains a challenging task due to variations in habitat, motion patterns, and species appearance. Traditional approaches typically require extensive model fine-tuning and heuristic design for each application scenario. In this work, we explore the potential of recent vision foundation models for zero-shot multi-animal tracking. By combining a Grounding Dino object detector with the Segment Anything Model 2 (SAM 2) tracker and carefully designed heuristics, we develop a tracking framework that can be applied to new datasets without any retraining or hyperparameter adaptation. Evaluations on ChimpAct, Bird Flock Tracking, AnimalTrack, and a subset of GMOT-40 demonstrate strong and consistent performance across diverse species and environments. The code is available at https://github.com/ecker-lab/SAM2-Animal-Tracking.

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Code Implementations(1)

ecker-lab/SAM2-Animal-TrackingOfficial100%
202PythonOct 17, 202511 months ago

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Cite this paper

@article{meier2025zeroshot,
  title  = {Zero-Shot Multi-Animal Tracking in the Wild},
  author = {Jan Frederik Meier and Timo Lüddecke},
  year   = {2025},
  eprint = {2511.02591},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.02591v1}
}

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