Visual tracking via geometric particle filtering on the affine group with optimal importance functions

We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate-invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geometrically defined o...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 991 - 998
Main Authors Junghyun Kwon, Kyoung Mu Lee, Park, Frank C
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Filtering
Gaussian distribution
Motion estimation
Particle measurements
Particle tracking
Predictive models
Principal component analysis
Taylor series
Two dimensional displays
Video sequences
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Summary:We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate-invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geometrically defined optimal importance function, obtained explicitly via Taylor expansion of a principal component analysis based measurement function on Aff(2). The efficiency of our approach to tracking is demonstrated via comparative experiments.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206501