A principal component regression strategy for estimating motion

被引：0

作者：

Estrela, Vania V. ^{[1
]}

Da Silva Bassani, M. H. ^{[1
]}

de Assis, J. T. ^{[1
]}

机构：

[1] State Univ Western Rio de Janeiro UEZO, Rua Manoel Caldeira de Alvarenga 1203, BR-23070120 Campo Grande, RJ, Brazil

来源：

PROCEEDINGS OF THE SEVENTH IASTED INTERNATIONAL CONFERENCE ON VISUALIZATION, IMAGING, AND IMAGE PROCESSING | 2007年

关键词：

motion estimation; principal component regression; surveillance;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we derive a principal component regression (PCR) method for estimating the optical flow between frames of video sequences according to a pel-recursive manner. This is an easy alternative to dealing with mixtures of motion vectors due to the lack of too much prior information on their statistics (although they are supposed to be normal). The 2D motion vector estimation takes into consideration local image properties. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.

引用

页码：224 / +

页数：3

共 16 条

[1]

[Anonymous], 1986, Principle Component Analysis

[2] A PEL-RECURSIVE WIENER-BASED DISPLACEMENT ESTIMATION ALGORITHM [J].