Challenging situations for background subtraction algorithms
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Data
2019-05-01
Autores
Sanches, Silvio R. R.
Oliveira, Claiton
Sementille, Antonio C. [UNESP]
Freire, Valdinei
Título da Revista
ISSN da Revista
Título de Volume
Editor
Springer
Resumo
Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.
Descrição
Palavras-chave
Background subtraction, Foreground extraction, Algorithm evaluation, Challenging situation
Como citar
Applied Intelligence. Dordrecht: Springer, v. 49, n. 5, p. 1771-1784, 2019.