Visual Tracking via Geometric Particle Filtering on the Affine Group with Optimal Importance Functions

[ Information ]

2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2009, pp. 991-998.

[ Authors ]

Junghyun Kwon, Kyoung Mu Lee, and Frank C. Park

[ 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.