2024/06/14 by Risa Shinoda, Shinoda, Risa, Kaede Shiohara +1 · 1 citation
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Engineering · #Advanced Chemical Sensor Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Food Supply Chain Traceability #Identification and Quantification in Food
paper · pdf · doi:10.48550/arxiv.2406.09647
openalex publication_date 2024/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.