Computer vision applications based on videos often require the detection of moving objects
in their first step. Background subtraction is then applied in order to separate the background …

A central goal of animal ecology is to observe species in the natural world. The cost and
challenge of data collection often limit the breadth and scope of ecological study. Ecologists …

It is desirable for detection and classification algorithms to generalize to unfamiliar
environments, but suitable benchmarks for quantitatively studying this phenomenon are not …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Camera traps enable the automatic collection of large quantities of image data. Ecologists
use camera traps to monitor animal populations all over the world. In order to estimate the …

Biologists all over the world use camera traps to monitor biodiversity and wildlife population
density. The computer vision community has been making strides towards automating the …

Background subtraction, although being a very well-established field, has required
significant research efforts to tackle unsolved challenges and to accelerate the progress …

Abstract Moving Object Detection (MOD) is a fundamental step for many computer vision
applications. MOD becomes very challenging when a video sequence captured from a static …

Recovering the low-rank and sparse components from a given matrix is a challenging
problem that has many real applications. This paper proposes a novel algorithm to address …

Camera traps are a valuable tool for studying biodiversity, but research using this data is
limited by the speed of human annotation. With the vast amounts of data now available it is …