Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring

Hung Nguyen, Sarah J. Maclagan, Tu Dinh Nguyen, Thin Nguyen, Paul Flemons, Kylie Andrews, Euan G. Ritchie, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

122 Citations (Scopus)


Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions. Automatic covert cameras or “camera traps” are being an increasingly popular tool for wildlife monitoring due to their effectiveness and reliability in collecting data of wildlife unobtrusively, continuously and in large volume. However, processing such a large volume of images and videos captured from camera traps manually is extremely expensive, time-consuming and also monotonous. This presents a major obstacle to scientists and ecologists to monitor wildlife in an open environment. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this paper a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labeled dataset from Wildlife Spotter project, done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, to train a computational system capable of filtering animal images and identifying species automatically. Our experimental results achieved an accuracy at 96.6% for the task of detecting images containing animal, and 90.4% for identifying the three most common species among the set of images of wild animals taken in South-central Victoria, Australia, demonstrating the feasibility of building fully automated wildlife observation. This, in turn, can therefore speed up research findings, construct more efficient citizen science-based monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
Subtitle of host publicationTokyo, Japan 19-21 October 2017
EditorsTakashi Washio, Joao Gama
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781509050048
ISBN (Print)9781509050055
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Conference on Data Science and Advanced Analytics 2017 - Tokyo, Japan
Duration: 19 Oct 201721 Oct 2017
Conference number: 4th (Proceedings)


ConferenceIEEE International Conference on Data Science and Advanced Analytics 2017
Abbreviated titleDSAA 2017
Internet address


  • Animal recognition
  • Citizen science
  • Convolutional neural networks
  • Deep learning
  • Large scale image classification
  • Wildlife monitoring

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