AnimalClue: Recognizing Animals by their Traces
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/
6889915e31e1218a089281c3
Risa Shinoda
6889915e31e1218a089281c4
Nakamasa Inoue
6889915e31e1218a089281c5
Iro Laina
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Christian Rupprecht
6889915e31e1218a089281c7
Hirokatsu Kataoka
AnimalClue: Recognizing Animals by their Traces
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/
AnimalClue is a large-scale dataset for species identification from indirect evidence images, addressing challenges in classification and segmentation tasks.
bounding boxes segmentation masks classification detection instance segmentation