Title: Camera-trap images segmentation using multi-layer robust principal component analysis,
Abstract: 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.