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Visual tracking via geometric particle filtering on the affine group with optimal importance functions
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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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 coordinateinvariant 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
doi:10.1109/cvpr.2009.5206501
dblp:conf/cvpr/KwonLP09
fatcat:hj7zv6trzraphpsckod6crrfke