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2016 | OriginalPaper | Chapter

Animal Identification in Low Quality Camera-Trap Images Using Very Deep Convolutional Neural Networks and Confidence Thresholds

Authors : Alexander Gomez, German Diez, Augusto Salazar, Angelica Diaz

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

Monitoring animals in the wild without disturbing them is possible using camera trapping framework. Automatic triggered cameras, which take a burst of images of animals in their habitat, produce great volumes of data, but often result in low image quality. This high volume data must be classified by a human expert. In this work a two step classification is proposed to get closer to an automatic and trustfully camera-trap classification system in low quality images. Very deep convolutional neural networks were used to distinguish images, firstly between birds and mammals, secondly between mammals sets. The method reached \(97.5\%\) and \(90.35\%\) in each task. An alleviation mode using a confidence threshold of automatic classification is proposed, allowing the system to reach \(100\%\) of performance traded with human work.

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Metadata
Title
Animal Identification in Low Quality Camera-Trap Images Using Very Deep Convolutional Neural Networks and Confidence Thresholds
Authors
Alexander Gomez
German Diez
Augusto Salazar
Angelica Diaz
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_67

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