Affine object tracking with kernel-based spatial-color representation

This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise rep...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 293 - 300 vol. 1
Main Authors Haihong Zhang, Weimin Huang, Zhiyong Huang, Liyuan Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Computer vision
Deformable models
Expectation-maximization algorithms
Gaussian distribution
Histograms
Kernel
Motion estimation
Parameter estimation
Target tracking
Videos
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Summary:This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise representation; the precise representation enables tracking objects in highly-cluttered environment. The basis of the method is a kernel-based similarity measure called affine matching that describes the relationship between image regions with respect to affine transformation parameters. Based on the similarity measure, a mathematical solution is derived for estimating the transformation parameters for moving objects in videos. Various experiments have yielded positive results.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.65