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  4. Towards Automatic Detection of Animals in Camera-Trap Images
 
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2018
Conference Paper
Titel

Towards Automatic Detection of Animals in Camera-Trap Images

Abstract
In recent years the world's biodiversity is declining on an unprecedented scale. Many species are endangered and remaining populations need to be protected. To overcome this agitating issue, biologist started to use remote camera devices for wildlife monitoring and estimation of remaining population sizes. Unfortunately, the huge amount of data makes the necessary manual analysis extremely tedious and highly cost intensive. In this paper we re-train and apply two state-of-the-art deep-learning based object detectors to localize and classify Serengeti animals in camera-trap images. Furthermore, we thoroughly evaluate both algorithms on a self-established dataset and show that the combination of the results of both detectors can enhance overall mean average precision. In contrast to previous work our approach is not only capable of classifying the main species in images but can also detect them and therefore count the number of individuals which is in fact an important information for biologists, ecologists, and wildlife epidemiologists.
Author(s)
Loos, Alexander
Weigel, Christin
Koehler, Mona
Hauptwerk
26th European Signal Processing Conference, EUSIPCO 2018
Konferenz
European Signal Processing Conference (EUSIPCO) 2018
Thumbnail Image
DOI
10.23919/EUSIPCO.2018.8553439
Language
English
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Fraunhofer-Institut für Digitale Medientechnologie IDMT
Tags
  • animal

  • biology computing

  • camera

  • camera-trap image

  • computer vision

  • deep-learning based o...

  • detector

  • image classification

  • learning (artificial ...

  • main species

  • metadata

  • object detection

  • object recognition

  • population size

  • remote camera device

  • Serengeti animal

  • sociology

  • statistic

  • wildlife monitoring

  • zoology

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