The DOI matches its Crossref record; 18 of the 22 references it cites were found in a registry or an index.
An Animal Detection Pipeline for Identification
Abstract
This paper proposes a 5-component detection pipeline for use in a computer vision-based animal recognition system. The end result of our proposed pipeline is a collection of novel annotations of interest (AoI) with species and view-point labels. These AoIs, for example, could be fed as the focused input data into an appearance-based animal identification system. The goal of our method is to increase the reliability and automation of animal censusing studies and to provide better ecological information to conservationists. Our method is able to achieve a localization mAP of 81.67%, a species and viewpoint annotation classification accuracy of 94.28% and 87.11%, respectively, and an AoI accuracy of 72.75% across 6 animal species of interest. We also introduce the Wildlife Image and Localization Dataset (WILD), which contains 5,784 images and 12,007 labeled annotations across 28 classification species and a variety of challenging, real-world detection scenarios.
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“The image analysis of Wildbook can start with images from any source—scientists, camera traps, drones, community scientists, or social media—and use ML and computer vision to detect multiple animals in the images100 to not only classify their species, but identify individual animals applying a suite of different algorithms101,147.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [100]
In the citing paper
“Similar to counting and reconstruction (see next section), computer vision recently emerged as a powerful alternative for automatic individual identification54,98–100.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [100]
Citations
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Cited by
Sorted by citations
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
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Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction
Hemmer, P., Westphal, M., Schemmer, M., Vetter, S., Vössing, M., & Satzger, G. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584052 · Cited by 136
Metadata
- Authors
- Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- Kitware (United States) (US)
- Princeton University (US)
- University of Illinois Chicago (US)
- ORCID
- Venue
- 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2018-03
- Pages
- 1075-1083
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2802043043
- Cited by
- 103
- References
- 22
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 20 · 2023: 17 · 2022: 24 · 2021: 10 · 2020: 15 · 2019: 4
- FWCI
- 2.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- animal detection, animal identification, wildlife census, species annotation, animal localization, conservation
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