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

Author

Junghyun Kwon ; Kyoung Mu Lee ; Park, F.C.

Author_Institution

Dept. of EECS, Seoul Nat. Univ., Seoul, South Korea

fYear

2009

fDate

20-25 June 2009

Firstpage

991

Lastpage

998

Abstract

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.

Keywords

affine transforms; image motion analysis; image sequences; particle filtering (numerical methods); principal component analysis; tracking filters; video signal processing; 2-D affine motion; Taylor expansion; coordinate-invariant particle filtering; geometric method; optimal importance function; principal component analysis; video sequence; visual tracking; Filtering; Gaussian distribution; Motion estimation; Particle measurements; Particle tracking; Predictive models; Principal component analysis; Taylor series; Two dimensional displays; Video sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on

Conference_Location

Miami, FL

ISSN

1063-6919

Print_ISBN

978-1-4244-3992-8

Type

conf

DOI

10.1109/CVPR.2009.5206501

Filename

5206501