OpenAnimalTracks

Animal habitat surveys play a critical role in preserving the biodiversity of the land. One of the effective ways to gain insights into animal habitats involves identifying animal footprints, which offers valuable information about species distribution, abundance, and behavior.
However, due to the scarcity of animal footprint images, there are no well-maintained public datasets, preventing recent advanced techniques in computer vision from being applied to animal tracking. In this paper, we introduce OpenAnimalTracks dataset, the first publicly available labeled dataset designed to facilitate the automated classification and detection of animal footprints. It contains various footprints from 18 wild animal species.
Moreover, we build benchmarks for species classification and detection and show the potential of automated footprint identification with representative classifiers and detection models. We find SwinTransformer achieves a promising classification result, reaching 69.41% in terms of the averaged accuracy. Faster-RCNN achieves mAP of 0.295. We hope our dataset paves the way for automated animal tracking techniques, enhancing our ability to protect and manage biodiversity. Our code is available on GitHub (https://github.com/dahlian00/OpenAnimalTracks). Please fill in the Google Form (https://forms.gle/KRfVyjbKDTqtCPu36) to confirm that the usage is for research purposes.

野生动物栖息地调查对保护陆地生物多样性至关重要。而通过识别动物足迹以深入了解其栖息地，是行之有效的途径之一，该方法可提供关于物种分布、种群丰度及行为模式的宝贵信息。然而，由于动物足迹图像较为稀缺，目前尚无维护完善的公开数据集，这阻碍了计算机视觉领域的先进技术在动物追踪任务中的应用。本文提出的OpenAnimalTracks数据集，是首个公开可用的带标注数据集，旨在助力动物足迹的自动化分类与检测任务。该数据集涵盖18种野生动物的各类足迹样本。此外，我们构建了针对物种分类与检测任务的基准测试集，并通过代表性分类器与检测模型验证了自动化足迹识别的可行性。经实验发现，SwinTransformer取得了优异的分类性能，平均分类精度达69.41%；Faster-RCNN的平均精度均值（mean Average Precision，mAP）为0.295。我们期望本数据集能够为自动化动物追踪技术的发展铺平道路，进而提升人类保护与管理生物多样性的能力。本研究的代码已开源至GitHub（https://github.com/dahlian00/OpenAnimalTracks）。请填写该谷歌表单（https://forms.gle/KRfVyjbKDTqtCPu36）以确认您将数据集用于科研用途。