Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Abstract Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in the...

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Bibliographic Details
Main Authors: Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric A. Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. VerCauteren, Jeff Clune, Ryan S. Miller
Format: Article
Language:English
Published: Wiley 2020-10-01
Series:Ecology and Evolution
Subjects:
computer vision
deep convolutional neural networks
image classification
machine learning
motion‐activated camera
R package
Online Access:https://doi.org/10.1002/ece3.6692

Internet

https://doi.org/10.1002/ece3.6692

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