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 |
The following license files are associated with this item: