OpenAnimalTracks: A Dataset for Animal Track Recognition

TL;DR

OpenAnimalTracks introduces the first public dataset of animal footprints from 18 species, enabling automated classification and detection to advance wildlife monitoring and biodiversity conservation.

Contribution

The paper presents a new publicly available dataset for animal footprint recognition and establishes benchmarks for classification and detection tasks.

Findings

SwinTransformer achieves 69.41% accuracy in species classification.

Faster-RCNN attains a mAP of 0.295 for footprint detection.

The dataset facilitates future research in automated animal tracking.

Abstract

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.