动物线索:通过痕迹识别动物
AnimalClue: Recognizing Animals by their Traces
July 27, 2025
作者: Risa Shinoda, Nakamasa Inoue, Iro Laina, Christian Rupprecht, Hirokatsu Kataoka
cs.AI
摘要
野生动物观察在生物多样性保护中扮演着重要角色,这要求我们采用稳健的方法来监测野生动物种群及种间互动。近年来,计算机视觉领域的显著进展极大地推动了基础野生动物观察任务的自动化,如动物检测与物种识别。然而,尽管通过足迹、粪便等间接证据准确识别物种对于野生动物监测至关重要,这一领域仍相对缺乏深入探索。为填补这一空白,我们推出了AnimalClue,首个基于间接证据图像进行物种识别的大规模数据集。该数据集包含159,605个边界框,涵盖五类间接线索:足迹、粪便、卵、骨骼和羽毛,覆盖了968个物种、200个科及65个目。每张图像均标注有物种级别标签、边界框或分割掩码,以及包括活动模式和栖息地偏好在内的细粒度特征信息。与现有主要关注直接视觉特征(如动物外观)的数据集不同,AnimalClue因需识别更为细致和微妙的视觉特征,为分类、检测及实例分割任务带来了独特挑战。在实验中,我们广泛评估了代表性视觉模型,并识别出从动物痕迹进行识别时的关键挑战。我们的数据集与代码已公开于https://dahlian00.github.io/AnimalCluePage/。
English
Wildlife observation plays an important role in biodiversity conservation,
necessitating robust methodologies for monitoring wildlife populations and
interspecies interactions. Recent advances in computer vision have
significantly contributed to automating fundamental wildlife observation tasks,
such as animal detection and species identification. However, accurately
identifying species from indirect evidence like footprints and feces remains
relatively underexplored, despite its importance in contributing to wildlife
monitoring. To bridge this gap, we introduce AnimalClue, the first large-scale
dataset for species identification from images of indirect evidence. Our
dataset consists of 159,605 bounding boxes encompassing five categories of
indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968
species, 200 families, and 65 orders. Each image is annotated with
species-level labels, bounding boxes or segmentation masks, and fine-grained
trait information, including activity patterns and habitat preferences. Unlike
existing datasets primarily focused on direct visual features (e.g., animal
appearances), AnimalClue presents unique challenges for classification,
detection, and instance segmentation tasks due to the need for recognizing more
detailed and subtle visual features. In our experiments, we extensively
evaluate representative vision models and identify key challenges in animal
identification from their traces. Our dataset and code are available at
https://dahlian00.github.io/AnimalCluePage/