2016
DOI: 10.1016/j.imavis.2015.11.006
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Background modeling using Object-based Selective Updating and Correntropy adaptation

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Cited by 8 publications
(3 citation statements)
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“…In order to handle such dynamic background, researchers have extended the temporal approach to develop the spatio-temporal models which is presented in [55]. Alvarez et al [26] have proposed an adaptive background model within an adaptive learning framework considering the Spatio temporal relationships among pixels. Recently, authors [24] have attempted to minimize the effect of different video irregularities like dynamic background, change in illuminations, video noise.…”
Section: Related Workmentioning
confidence: 99%
“…In order to handle such dynamic background, researchers have extended the temporal approach to develop the spatio-temporal models which is presented in [55]. Alvarez et al [26] have proposed an adaptive background model within an adaptive learning framework considering the Spatio temporal relationships among pixels. Recently, authors [24] have attempted to minimize the effect of different video irregularities like dynamic background, change in illuminations, video noise.…”
Section: Related Workmentioning
confidence: 99%
“…On the basis of pixel for modelling background, background model can be classified into pixel-based model [7][8][9][10][11][12] and region-based model. [13][14][15][16][17][18] The pixelbased methods model observed scenes as a set of independent pixel processes, while the region-based methods represent background model by using inter-pixel relations. Among these methods, one of the most widely used methods for background modelling is the Gaussian Mixture Model (GMM).…”
Section: Introductionmentioning
confidence: 99%
“…The visual background model method proposed by Barnich and Droogenbroeck 12 has a simple model and good real-time, yet it needs to store a large number of pixel values, and ghosts easily appear in detection results. In Álvarez-Meza et al, 13 a novel adaptive background modelling method was proposed by using object-based selective updating and correntropy adaptation. The method can detect and track foreground entities based on pixel intensities and motion direction, but it is difficult to capture the optical motion information accurately in some case.…”
Section: Introductionmentioning
confidence: 99%