Perspectives on individual animal identification from biology and computer vision

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.

12 pages, 1 figure, 2 boxes and 1 table

References in corpus (7)

• A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
• HotSpotter - Patterned Species Instance Recognition
• Efficient Pipeline for Camera Trap Image Review
• Wildbook: Crowdsourcing, computer vision, and data science for conservation
• Towards Automatic Identification of Elephants in the Wild
• A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments
• The Sloop System for Individual Animal Identification with Deep Learning

Cited by in corpus (5)

• Seeing biodiversity: perspectives in machine learning for wildlife conservation
• The iWildCam 2020 Competition Dataset
• The Animal ID Problem: Continual Curation
• Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset
• DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation