The Visual Computer

, Volume 35, Issue 3, pp 335–347 | Cite as

Camera-trap images segmentation using multi-layer robust principal component analysis

  • Jhony-Heriberto Giraldo-ZuluagaEmail author
  • Augusto Salazar
  • Alexander Gomez
  • Angélica Diaz-Pulido
Original Article


The segmentation of animals from camera-trap images is a difficult task. To illustrate, there are various challenges due to environmental conditions and hardware limitation in these images. We proposed a multi-layer robust principal component analysis (multi-layer RPCA) approach for background subtraction. Our method computes sparse and low-rank images from a weighted sum of descriptors, using color and texture features as case of study for camera-trap images segmentation. The segmentation algorithm is composed of histogram equalization or Gaussian filtering as pre-processing, and morphological filters with active contour as post-processing. The parameters of our multi-layer RPCA were optimized with an exhaustive search. The database consists of camera-trap images from the Colombian forest taken by the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt. We analyzed the performance of our method in inherent and therefore challenging situations of camera-trap images. Furthermore, we compared our method with some state-of-the-art algorithms of background subtraction, where our multi-layer RPCA outperformed these other methods. Our multi-layer RPCA reached 76.17 and 69.97% of average fine-grained F-measure for color and infrared sequences, respectively. To our best knowledge, this paper is the first work proposing multi-layer RPCA and using it for camera-trap images segmentation.


Camera-trap images Multi-layer robust principal component analysis Background subtraction Image segmentation 



This work was supported by the Colombian National Fund for Science, Technology and Innovation, Francisco José de Caldas - COLCIENCIAS (Colombia). Project No. 111571451061.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Grupo de Investigación SISTEMIC, Facultad de IngenieríaUniversidad de AntioquiaMedellínColombia
  2. 2.Instituto de Investigación de Recursos Biológicos Alexander von HumboldtBogotá, D.C.Colombia

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