  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 dataset and code
are available at https://github.com/dahlian00/OpenAnimalTracks.
