Literature DB >> 34050741

Perspectives on individual animal identification from biology and computer vision.

Maxime Vidal1,2, Nathan Wolf3, Beth Rosenberg3, Bradley P Harris3, Alexander Mathis1,2.   

Abstract

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.
© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.

Keywords:  Animal Biometrics; Animal Identification; Computer Vision; Deep Learning; Re-Identification

Year:  2021        PMID: 34050741     DOI: 10.1093/icb/icab107

Source DB:  PubMed          Journal:  Integr Comp Biol        ISSN: 1540-7063            Impact factor:   3.326


  3 in total

1.  Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments.

Authors:  Markus Marks; Jin Qiuhan; Oliver Sturman; Lukas von Ziegler; Sepp Kollmorgen; Wolfger von der Behrens; Valerio Mante; Johannes Bohacek; Mehmet Fatih Yanik
Journal:  Nat Mach Intell       Date:  2022-04-21

2.  Evaluating Cognitive Enrichment for Zoo-Housed Gorillas Using Facial Recognition.

Authors:  Otto Brookes; Stuart Gray; Peter Bennett; Katy V Burgess; Fay E Clark; Elisabeth Roberts; Tilo Burghardt
Journal:  Front Vet Sci       Date:  2022-05-18

Review 3.  Perspectives in machine learning for wildlife conservation.

Authors:  Devis Tuia; Benjamin Kellenberger; Sara Beery; Blair R Costelloe; Silvia Zuffi; Benjamin Risse; Alexander Mathis; Mackenzie W Mathis; Frank van Langevelde; Tilo Burghardt; Roland Kays; Holger Klinck; Martin Wikelski; Iain D Couzin; Grant van Horn; Margaret C Crofoot; Charles V Stewart; Tanya Berger-Wolf
Journal:  Nat Commun       Date:  2022-02-09       Impact factor: 14.919

  3 in total

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