Kernel selection for mean shift background tracking in video surveillance

CODRUT, Ianăşi; TOMA, Corneliu; GUI, Vasile; PESCARU, Dan

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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	2019-10-29T08:44:24Z
dc.date.available	2019-10-29T08:44:24Z
dc.date.issued	2005
dc.identifier.citation	CODRUT Ianăşi, Corneliu TOMA, Vasile GUI et al. 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. 90-93. ISBN 9975-66-038-X.	en_US
dc.identifier.isbn	9975-66-038-X
dc.identifier.uri	http://repository.utm.md/handle/5014/5541
dc.description.abstract	Nonparametric kernel density estimation has been successfully used in modelling 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	background	en_US
dc.subject	motion	en_US
dc.subject	kernel density estimation	en_US
dc.subject	video surveillance	en_US
dc.title	Kernel selection for mean shift background tracking in video surveillance	en_US
dc.type	Article	en_US