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

TL;DR

BuckTales introduces a comprehensive UAV dataset for multi-object tracking and re-identification of wild antelopes, enabling advancements in automated wildlife monitoring and behavioural analysis in natural habitats.

Contribution

This paper presents the first large-scale UAV dataset for wild animal MOT and Re-ID, specifically for blackbuck antelopes, with extensive annotations and benchmark results.

Findings

Over 1.2 million annotations in the dataset

Benchmarking of state-of-the-art tracking methods

Dataset facilitates long-term, scalable wildlife monitoring

Abstract

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.