# BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes arXiv: https://arxiv.org/abs/2411.06896 ยท HTML: https://mlsanity.com/paper/2411.06896/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 43 references that are in the map. - First posted on arXiv 11 Nov 2024. ## Builds on (most cited first, 16 of 16) - [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) - [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022) - [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022) - [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020) - [TAO: A Large-Scale Benchmark for Tracking Any Object](https://mlsanity.com/paper/2005.10356/) (2020) - [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021) - [AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild](https://mlsanity.com/paper/2205.00158/) (2022) - [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023) - [GMOT-40: A Benchmark for Generic Multiple Object Tracking](https://mlsanity.com/paper/2011.11858/) (2020) - [Vehicle Re-identification in Aerial Imagery: Dataset and Approach](https://mlsanity.com/paper/1904.01400/) (2019) - [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017) - [3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture](https://mlsanity.com/paper/2303.13174/) (2023) - [3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking](https://mlsanity.com/paper/2308.15316/) (2023) - [Synthetic Data-based Detection of Zebras in Drone Imagery](https://mlsanity.com/paper/2305.00432/) (2023) - [Deep Learning for Automatic Detection and Facial Recognition in Japanese Macaques: Illuminating Social Networks](https://mlsanity.com/paper/2310.06489/) (2023) ## Built on by (most cited first, 14 of 14) - [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025) - [WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs](https://mlsanity.com/paper/2504.10165/) (2025) - [Towards Application-Specific Evaluation of Vision Models: Case Studies in Ecology and Biology](https://mlsanity.com/paper/2505.02825/) (2025) - [DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO](https://mlsanity.com/paper/2503.04698/) (2025) - [MMLA: Multi-Environment, Multi-Species, Low-Altitude Drone Dataset](https://mlsanity.com/paper/2504.07744/) (2025) - [SmartWilds: Multimodal Wildlife Monitoring Dataset](https://mlsanity.com/paper/2509.18894/) (2025) - [MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking](https://mlsanity.com/paper/2510.12565/) (2025) - [Deep in the Jungle: Towards Automating Chimpanzee Population Estimation](https://mlsanity.com/paper/2601.22917/) (2026) - [CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild](https://mlsanity.com/paper/2603.25524/) (2026) - [WildBox: A Dataset and Benchmark for Aerial Monocular 3D Detection of African Savanna Wildlife](https://mlsanity.com/paper/2606.21309/) (2026) - [HieDG: A Hierarchical Discrete Geometry-Guided Framework for Multi-Animal Tracking](https://mlsanity.com/paper/2607.00494/) (2026) - [When One Modality Is Not Enough: Multimodal Sex and Life-Stage Classification of Red Deer from Aerial RGB-Thermal Video](https://mlsanity.com/paper/2608.06973/) (2026) - [Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance](https://mlsanity.com/paper/2608.18602/) (2026) - [Re-engineering SORT-based algorithms for low-cost small object tracking from omnidirectional footage](https://mlsanity.com/paper/2609.07547/) (2026) ## Abstract (arXiv) Understanding animal behaviour is central to predicting, understanding, and mitigating impacts of natural and anthropogenic changes on animal populations and ecosystems. However, the challenges of acquiring and processing long-term, ecologically relevant data in wild settings have constrained the scope of behavioural research. The increasing availability of Unmanned Aerial Vehicles (UAVs), coupled with advances in machine learning, has opened new opportunities for wildlife monitoring using aerial tracking. However, limited availability of datasets with wild animals in natural habitats has hindered progress in automated computer vision solutions for long-term animal tracking. Here we introduce BuckTales, the first large-scale UAV dataset designed to solve multi-object tracking (MOT) and re-identification (Re-ID) problem in wild animals, specifically the mating behaviour (or lekking) of blackbuck antelopes. Collected in collaboration with biologists, the MOT dataset includes over 1.2 million annotations including 680 tracks across 12 high-resolution (5.4K) videos, each averaging 66 seconds and featuring 30 to 130 individuals. The Re-ID dataset includes 730 individuals captured with two UAVs simultaneously. The dataset is designed to drive scalable, long-term animal behaviour tracking using multiple camera sensors. By providing baseline performance with two detectors, and benchmarking several state-of-the-art tracking methods, our dataset reflects the real-world challenges of tracking wild animals in socially and ecologically relevant contexts. In making these data widely available, we hope to catalyze progress in MOT and Re-ID for wild animals, fostering insights into animal behaviour, conservation efforts, and ecosystem dynamics through automated, long-term monitoring.