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

arXiv: https://arxiv.org/abs/1701.08180 · HTML: https://mlsanity.com/paper/1701.08180/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [The iWildCam 2021 Competition Dataset](https://mlsanity.com/paper/2105.03494/) (2021)
- [CATALOG: A Camera Trap Language-guided Contrastive Learning Model](https://mlsanity.com/paper/2412.10624/) (2024)
- [Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos](https://mlsanity.com/paper/2207.06440/) (2022)
- [WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift](https://mlsanity.com/paper/2601.00993/) (2026)
- [Inteligencia Artificial para la conservaci'on y uso sostenible de la biodiversidad, una visi'on desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia)](https://mlsanity.com/paper/2503.14543/) (2025)

## Abstract (arXiv)

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 Investigaci'on de Recursos Biol'ogicos 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.