Generación automática de conjuntos de evaluación de camuflaje
Author
Álvarez Balanya, SergioEntity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesDate
2018-07Subjects
Jung’s theorem; background subtraction; camouflage; TelecomunicacionesEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
Background subtraction has become a key step in several computer vision algorithms. There
are plenty of studies proposing different and varied approaches. However, the problem of
background subtraction is not yet fully addressed. One reason might be the fact that each method
has been developed for different tasks, e.g. video surveillance or optical motion capture.
The recent appearance of comprehensive datasets provides a common framework for evaluating
background subtraction algorithms. These datasets present a balanced repertoire of sequences
in which common challenges are present. This leads to extensive overall scores in which robustness
against different challenges is considered, but not particularized to these challenges. A
particularly barely studied challenge, and the focus of our work, is camouflage: the resemblance
between background and foreground samples. The research community agrees that there isn’t
yet a commonly accepted approach to handle camouflage.
In this work, we propose a novel solution for modeling camouflage based on the Jung’s
theorem. Based on this solution, we generate camouflage likelihoods for every foreground pixel
in a sequence using available ground-truth information to discriminate the background from the
foreground.
The evaluation of the proposed solution is performed in discrepancy terms by thresholding
the camouflage likelihoods to obtain a binary mask on which we apply classical classification
metrics. Thereby, we are able to further analyze the effect of the features selected by different
background subtraction algorithms in handling camouflage. Furthermore, the proposed solution
also permits the ranking of a set of sequences in terms of camouflage.
The experiments carried out on the popular CDNET2014 dataset suggest that the use of
certain alternative features to color—e,g, motion—is beneficial to robustly handle camouflage.
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