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Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer-assisted

Margarete Dytkowicz1,2, Marcello Tania1, Rachel Hinds3

  • 1FabLab Blue, Faculty of Technology and Bionics University of Applied Sciences Kleve Germany.

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This study demonstrates a 95.7% accurate method for identifying individual Eurasian beavers using their unique tail scale patterns. The Scale Invariant Feature Transform (SIFT) algorithm offers a non-invasive, semi-automated approach for ecological research.

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Area of Science:

  • Ecological sciences
  • Wildlife biology
  • Computer vision applications in ecology

Background:

  • Individual recognition is crucial in ecological studies.
  • Photograph-based methods offer non-invasive animal identification.
  • Advancements in computer vision enable faster analysis of large image datasets.

Purpose of the Study:

  • To evaluate the effectiveness of the Scale Invariant Feature Transform (SIFT) algorithm for individual beaver recognition.
  • To test a semi-automated method for analyzing tail pattern images.
  • To establish a reliable, non-invasive technique for monitoring Eurasian beaver populations.

Main Methods:

  • Applied the SIFT algorithm to 800 tail scale pattern images from 100 Eurasian beavers (Castor fiber).
  • Extracted scale patterns from dorsal tail images using open-source image processing software.
  • Utilized an 80% training and 20% testing data split for algorithm validation.

Main Results:

  • Achieved an overall recognition accuracy of 95.7%.
  • Demonstrated that individual scale patterns on the beaver's tail are unique and identifiable.
  • Confirmed the robustness of the SIFT algorithm to variations in image acquisition.

Conclusions:

  • The SIFT algorithm is a highly effective tool for distinguishing individual beavers based on tail scale patterns.
  • This method provides a scalable and non-invasive approach for wildlife monitoring and ecological research.
  • Photograph-based analysis using SIFT can significantly enhance the efficiency of large-scale animal identification studies.