Camera-trap images segmentation using multi-layer robust principal component analysis
By Jhony-Heriberto Giraldo-Zuluaga, Augusto Salazar, Alexander Gómez, A. Díaz‐Pulido. 2017
34 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Grant Van Horn
- Sara Beery
- Thierry Bouwmans
- Belmar García García
- Pietro Perona
- Luc Van Gool
- Siyu Yang
- Dan Morris
In Media Technology
- Belmar García García
- Dan Morris
- Pietro Perona
- Thierry Bouwmans
- Sara Beery
- Grant Van Horn
- Andrew Zisserman
- Li Fei-Fei
Citations in the corpus
- Recognition in Terra Incognita (2018, methodology): “[25] uses a multilayer RPCA-based method applied to day and night sequences.”
- Efficient Pipeline for Camera Trap Image Review (2019, methodology): “The computer vision community has been making strides towards automating the species classification challenge in camera traps [1, 2, 4–16], but it has proven difficult to to apply models trained in one region to images collected in different geographic areas.”
- The iWildCam 2019 Challenge Dataset (2019, background): “(4) Occlusion (5) Camouflage (6) Perspective”
- Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “…al. (2015) [362] CFD [215] - Difference RGB Illumination and Reflectance Estimation Hinds Surveillance Khorrami et al. (2012) [183] RPCA [57] - - Intensity - Giraldo-Zuluaga et al. (2017) [118] Multi-Layer RPCA [118] - - RGB - Giraldo-Zuluaga et al. (2017) [117] Multi-Layer RPCA [118] - - RGB…”