Cited by 103Advanced Neural Network ApplicationsWildlife Ecology and ConservationSmart Agriculture and AI

An Animal Detection Pipeline for Identification

Parham, Jason, Stewart, Charles, Crall, Jonathan, Rubenstein, Daniel, Holmberg, Jason, Berger-Wolf, Tanya·2018·2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

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.

Integrity signals

Registry

Crossref · 10.1109/wacv.2018.00123

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Wildbook image analysis can start with images from any source and use ML and computer vision to detect multiple animals, classify their species, and identify individual animals.

    “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]

  • Computer vision recently emerged as a powerful alternative for automatic individual identification.

    “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]

Cites

Flagged first, then by citations

4 of the 22 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

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)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review