Generación automática de conjuntos de evaluación de camuflaje
| Autor: | Álvarez Balanya, Sergio |
|---|---|
| Přispěvatelé: | Escudero-Viñolo, Marcos, Bescós, Jesús, UAM. Departamento de Tecnología Electrónica y de las Comunicaciones |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Biblos-e Archivo. Repositorio Institucional de la UAM instname |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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