Abstract
Foreground detection is the classical computer vision task of segmenting out moving object in a particular scene. Many algorithms have been proposed in the past decade for foreground detection. It is often hard to keep track of recent advances in a particular research field with the passage of time. An overview paper is an effective way for the researchers to compare several algorithms according to their strengths and weaknesses. There are several overview papers in the literature; however, they are somewhat obsolete. This overview paper covers the recent algorithms proposed in past 3–5 years except Gaussian Mixture Models (GMM). The aim and contribution of this overview paper is as follows: First, algorithms are classified in three different categories on the basis of choice of picture’s element, feature, and model. Then, each algorithm is summarized concisely. Furthermore, algorithms are compared quantitative and qualitatively using large realistic standard dataset. Paper is concluded with several promising directions for future research.
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This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (2016R1D1A1A02937579).
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Shahbaz, A., Kurnianggoro, L., Wahyono, Jo, KH. (2017). Recent Advances in the Field of Foreground Detection: An Overview. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_23
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