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Kernel selection for mean shift background tracking in video surveillance

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dc.contributor.author CODRUT, Ianăşi
dc.contributor.author TOMA, Corneliu
dc.contributor.author GUI, Vasile
dc.contributor.author PESCARU, Dan
dc.date.accessioned 2019-10-30T10:28:21Z
dc.date.available 2019-10-30T10:28:21Z
dc.date.issued 2005
dc.identifier.citation CODRUT, Ianăşi, TOMA, Corneliu, GUI, Vasile. Kernel selection for mean shift background tracking in video surveillance. In: Microelectronics and Computer Science: proc. of the 4th intern. conf., September 15-17, 2005. Chişinău, 2005, vol. 2, pp. 389-392. ISBN 9975-66-038-X. en_US
dc.identifier.isbn 9975-66-038-X
dc.identifier.uri http://repository.utm.md/handle/5014/5652
dc.description.abstract Nonparametric kernel density estimation has been successfully used in modeling the background statistics, in video surveillance, due to its capability to perform well without making any assumption about the form of the underlying distributions. To overcome the heavy computational load of the method, we recently proposed a fast approach based on a tracking mean shift estimator. In this paper we study the kernel selection problem for the mean shift background tracker. Comparative results for the Gaussian and Epanechnikov kernel are included. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject nonparametric kernel en_US
dc.subject kernel en_US
dc.subject video surveillance en_US
dc.subject background subtraction en_US
dc.subject motion segmentation en_US
dc.title Kernel selection for mean shift background tracking in video surveillance en_US
dc.type Article en_US


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