# Zero-Shot Multi-Animal Tracking in the Wild

arXiv: https://arxiv.org/abs/2511.02591 · HTML: https://mlsanity.com/paper/2511.02591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 30 of 34)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes](https://mlsanity.com/paper/2304.05170/) (2023)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory](https://mlsanity.com/paper/2411.11922/) (2024)
- [SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree](https://mlsanity.com/paper/2410.16268/) (2024)
- [A Distractor-Aware Memory for Visual Object Tracking with SAM2](https://mlsanity.com/paper/2411.17576/) (2024)
- [Unifying Short and Long-Term Tracking with Graph Hierarchies](https://mlsanity.com/paper/2212.03038/) (2022)
- [Multiple Object Tracking as ID Prediction](https://mlsanity.com/paper/2403.16848/) (2024)
- [AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild](https://mlsanity.com/paper/2205.00158/) (2022)
- [GMOT-40: A Benchmark for Generic Multiple Object Tracking](https://mlsanity.com/paper/2011.11858/) (2020)
- [Progressive End-to-End Object Detection in Crowded Scenes](https://mlsanity.com/paper/2203.07669/) (2022)
- [ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors](https://mlsanity.com/paper/2310.16447/) (2023)
- [PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition](https://mlsanity.com/paper/2401.13554/) (2024)
- [NetTrack: Tracking Highly Dynamic Objects with a Net](https://mlsanity.com/paper/2403.11186/) (2024)
- [SAM2MOT: A Novel Paradigm of Multi-Object Tracking by Segmentation](https://mlsanity.com/paper/2504.04519/) (2025)
- [AlphaChimp: Tracking and Behavior Recognition of Chimpanzees](https://mlsanity.com/paper/2410.17136/) (2024)

## Abstract (arXiv)

Multi-animal tracking is crucial for understanding animal ecology and behavior, yet remains challenging due to variations in habitat, motion patterns, and species appearance. Traditional approaches typically require extensive fine-tuning and heuristic design for each new scenario. In this work, we explore vision foundation models for zero-shot multi-animal tracking. Building on SAM2MOT, we combine Grounding DINO with the Segment Anything Model2 (SAM 2) and introduce three targeted modifications to adapt the framework to animal appearance and behavior without any retraining or hyperparameter tuning between datasets. We also evaluate the recent SAM3 model, but identify practical limitations that restrict its applicability to multi-animal tracking in the wild. Our method achieves state-of-the-art results across Chimp-Act, Bird Flock Tracking, AnimalTrack, and a subset of GMOT-40, demonstrating robust generalization across diverse species and environments. The code is available at https://github.com/ecker-lab/SAM2-Animal-Tracking.