# AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild

arXiv: https://arxiv.org/abs/2205.00158 · HTML: https://mlsanity.com/paper/2205.00158/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 69 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [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)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [TAO: A Large-Scale Benchmark for Tracking Any Object](https://mlsanity.com/paper/2005.10356/) (2020)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking](https://mlsanity.com/paper/1504.01942/) (2015)

## Built on by (most cited first, 30 of 37)

- [ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors](https://mlsanity.com/paper/2310.16447/) (2023)
- [NetTrack: Tracking Highly Dynamic Objects with a Net](https://mlsanity.com/paper/2403.11186/) (2024)
- [BoostTrack++: using tracklet information to detect more objects in multiple object tracking](https://mlsanity.com/paper/2408.13003/) (2024)
- [BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos](https://mlsanity.com/paper/2405.17698/) (2024)
- [BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes](https://mlsanity.com/paper/2411.06896/) (2024)
- [In Pursuit of Many: A Review of Modern Multiple Object Tracking Systems](https://mlsanity.com/paper/2209.04796/) (2022)
- [3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking](https://mlsanity.com/paper/2308.15316/) (2023)
- [Beyond MOT: Semantic Multi-Object Tracking](https://mlsanity.com/paper/2403.05021/) (2024)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [LaMOT: Language-Guided Multi-Object Tracking](https://mlsanity.com/paper/2406.08324/) (2024)
- [Z-GMOT: Zero-shot Generic Multiple Object Tracking](https://mlsanity.com/paper/2305.17648/) (2023)
- [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025)
- [CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming](https://mlsanity.com/paper/2312.08764/) (2023)
- [AlphaChimp: Tracking and Behavior Recognition of Chimpanzees](https://mlsanity.com/paper/2410.17136/) (2024)
- [OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking](https://mlsanity.com/paper/2410.17534/) (2024)
- [Hierarchical Instruction-aware Embodied Visual Tracking](https://mlsanity.com/paper/2505.20710/) (2025)
- [Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art](https://mlsanity.com/paper/2506.13457/) (2025)
- [GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring](https://mlsanity.com/paper/2512.07776/) (2025)
- [Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose Estimation](https://mlsanity.com/paper/2502.18214/) (2025)
- [TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT](https://mlsanity.com/paper/2409.02490/) (2024)
- [Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras](https://mlsanity.com/paper/2410.06698/) (2024)
- [LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking](https://mlsanity.com/paper/2505.12753/) (2025)
- [OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking](https://mlsanity.com/paper/2407.14047/) (2024)
- [Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking](https://mlsanity.com/paper/2410.09243/) (2024)
- [Shape-from-Template with Generalised Camera](https://mlsanity.com/paper/2508.13791/) (2025)
- [DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video](https://mlsanity.com/paper/2509.03499/) (2025)
- [POPCat: Propagation of particles for complex annotation tasks](https://mlsanity.com/paper/2406.17183/) (2024)
- [Multi-animal tracking in Transition: Comparative Insights into Established and Emerging Methods](https://mlsanity.com/paper/2509.11873/) (2025)
- [Lattice Boltzmann Model for Learning Real-World Pixel Dynamicity](https://mlsanity.com/paper/2509.16527/) (2025)
- [Zero-Shot Multi-Animal Tracking in the Wild](https://mlsanity.com/paper/2511.02591/) (2025)

## Abstract (arXiv)

Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks. To address this problem, we introduce AnimalTrack, a dedicated benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representative trackers. The evaluation results demonstrate that, not surprisingly, most of these trackers become degenerated due to the differences between pedestrians and animals in various aspects (e.g., pose, motion, and appearance), and more efforts are desired to improve multi-animal tracking. We hope that AnimalTrack together with evaluation and analysis will foster further progress on multi-animal tracking. The dataset and evaluation as well as our analysis will be made available at https://hengfan2010.github.io/projects/AnimalTrack/.