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Animal Scanner: Software for classifying humans, animals, and empty frames in camera trap images.

Hayder Yousif1, Jianhe Yuan1, Roland Kays2,3

  • 1Department of Electrical and Computer Engineering University of Missouri-Columbia Columbia Missouri.

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|March 9, 2019
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Summary

We developed computer vision algorithms to efficiently filter camera trap images, significantly reducing processing time and improving wildlife monitoring. This tool separates animal detections from empty or human images, saving valuable research time.

Keywords:
background subtractioncamera trap imagesdeep convolutional neural networkshuman–animal detectionwildlife monitoring

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Area of Science:

  • Ecology and Conservation
  • Computer Science
  • Wildlife Management

Background:

  • Camera traps are essential for noninvasive wildlife population sampling, generating millions of images per study.
  • Processing these large datasets is time-consuming due to false triggers and human presence.
  • Automated filtering is needed to manage the vast volume of camera trap data.

Purpose of the Study:

  • To develop and present a computer vision tool for automated camera trap image filtering.
  • To accurately separate animal detections from empty frames and human images.
  • To significantly reduce the time and effort required for wildlife monitoring using camera traps.

Main Methods:

  • Coupling foreground object segmentation (background subtraction) with deep learning classification.
  • Developing software as Matlab GUI and C++ command-line tools.
  • Implementing algorithms for human-animal detection and empty frame classification.

Main Results:

  • Achieved a 14x reduction in execution time for image processing.
  • Successfully removed 54% of false-trigger sequences without impacting animal/human data.
  • Attained 99.58% accuracy in classifying empty versus object images on the Serengeti dataset.

Conclusions:

  • The developed computer vision tool offers substantial time savings for processing large camera trap image datasets.
  • This technology enhances the efficiency and scalability of wildlife monitoring efforts.
  • It represents the first computer vision tool specifically designed for camera trap image analysis.