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.

翻译：理解动物行为对于预测、理解并缓解自然与人为变化对动物种群及生态系统的影响至关重要。然而，在野外环境中获取和处理长期生态相关数据所面临的挑战，限制了行为研究的范围。随着无人机（UAV）的日益普及以及机器学习的进步，利用空中跟踪技术进行野生动物监测开辟了新的机遇。然而，自然栖息地中野生动物数据集的稀缺，阻碍了长期动物自动跟踪计算机视觉解决方案的发展。本文介绍BuckTales，首个专为解决野生动物多目标跟踪（MOT）与重识别（Re-ID）问题而设计的大规模无人机数据集，重点关注黑羚羊的求偶行为（即lekking）。该数据集与生物学家合作采集，其中MOT数据集包含超过120万个标注，涵盖12段高分辨率（5.4K）视频中的680条轨迹，每段视频平均时长为66秒，包含30至130个个体。Re-ID数据集包含由两架无人机同步采集的730个个体。本数据集旨在推动利用多相机传感器实现可扩展的长期动物行为跟踪。通过提供两种检测器的基线性能，并对多种先进跟踪方法进行基准测试，我们的数据集反映了在社交与生态相关背景下跟踪野生动物所面临的实际挑战。我们希望通过广泛公开这些数据，推动野生动物MOT与Re-ID研究的进展，借助自动化长期监测深化对动物行为、保护工作及生态系统动态的理解。