Automated Visual Large Scale Monitoring of Faunal Biodiversity

Bernd Radig, Paul Bodesheim, Dimitri Korsch, Joachim Denzler, Timm Haucke, Morris Klasen, Volker Steinhage

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract: To observe biodiversity, the variety of plant and animal life in the world or in a particular habitat, human observers make the most common examinations, often assisted by technical equipment. Measuring objectively the number of different species of animals, plants, fungi, and microbes that make up the ecosystem can be difficult. In order to monitor changes in biodiversity, data have to be compared across space and time. Cameras are an essential sensor to determine the species range, abundance, and behavior of animals. The millions of recordings from camera traps set up in natural environments can no longer be analyzed by biologists. We started research on doing this analysis automatically without human interaction. The focus of our present sensor is on image capture of wildlife and moths. Special hardware elements for the detection of different species are designed, implemented, tested, and improved, as well as the algorithms for classification and counting of samples from images and image sequences, e.g., to calculate presence, absence, and abundance values or the duration of characteristic activities related to the spatial mobilities. For this purpose, we are developing stereo camera traps that allow spatial reconstruction of the observed animals. This allows three-dimensional coordinates to be recorded and the shape to be characterized. With this additional feature data, species identification and movement detection are facilitated. To classify and count moths, they are attracted to an illuminated screen, which is then photographed at intervals by a high-resolution color camera. To greatly reduce the volume of data, redundant elements and elements that are consistent from image to image are eliminated. All design decisions take into account that at remote sites and in fully autonomous operation, power supply on the one hand and possibilities for data exchange with central servers on the other hand are limited. Installation at hard-to-reach locations requires a sophisticated and demanding system design with an optimal balance between power requirements, bandwidth for data transmission, required service and operation in all environmental conditions for at least ten years.

Original languageEnglish
Pages (from-to)477-488
Number of pages12
JournalPattern Recognition and Image Analysis
Volume31
Issue number3
DOIs
StatePublished - Jul 2021

Keywords

  • active learning
  • animal detection
  • anomaly detection
  • biodiversity monitoring
  • camera trapping with color and depth sensors
  • deep learning
  • depth estimation
  • fine-grained recognition
  • incremental learning
  • lifelong learning
  • motion estimation in color and depth videos
  • multi-object tracking
  • novelty detection
  • sensor networks
  • species classification

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