BuckTales: A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes

Hemal Naik, Junran Yang, Dipin Das, Margaret C Crofoot, Akanksha Rathore, Vivek H Sridhar

Keywords: Dataset, MOT, Re-Identification, UAVs, Animals, Wild, Antelopes

TL;DR: The paper offers a unique dataset for developing multi-object tracking and re-identification with large group of wild animals using multiple UAVs for applications in research and conservation.

Abstract:

Understanding animal behaviour is central to predicting, understanding, and miti-
gating impacts of natural and anthropogenic changes on animal populations and
ecosystems. However, the challenges of acquiring and processing long-term, eco-
logically relevant data in wild settings have constrained the scope of behavioural
research. The increasing availability of Unmanned Aerial Vehicles (UAVs), cou-
pled with advances in machine learning, has opened new opportunities for wildlife
monitoring using aerial tracking. However, the 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 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 behavior 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, conser-
vation efforts, and ecosystem dynamics through automated, long-term monitoring.

Submission Number: 205