Optimizing the automated recognition of individual animals to support population monitoring.
Hotspotter
I3S‐Pattern
Lycaon pictus
WildID
automated individual recognition
photographic identification
Journal
Ecology and evolution
ISSN: 2045-7758
Titre abrégé: Ecol Evol
Pays: England
ID NLM: 101566408
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
09
03
2023
revised:
18
06
2023
accepted:
21
06
2023
medline:
5
7
2023
pubmed:
5
7
2023
entrez:
5
7
2023
Statut:
epublish
Résumé
Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, noninvasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogs is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages. In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I
Identifiants
pubmed: 37404703
doi: 10.1002/ece3.10260
pii: ECE310260
pmc: PMC10316465
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e10260Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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