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Visual Tracking via Particle Filtering on the Affine Group

Seoul National University, Seoul, Korea

^* To whom correspondence should be addressed. E-mail: fcp{at}snu.ac.kr.

	Abstract

We present a particle filtering algorithm for visual tracking, in which the state equations for the object motion evolve on the two-dimensional affine group. We first formulate, in a coordinate-invariant and geometrically meaningful way, particle filtering on the affine group that allows for combined state–covariance estimation. Measurement likelihoods are also calculated from the image covariance descriptors using incremental principal geodesic analysis, a generalization of principal component analysis to curved spaces. Comparative visual tracking studies demonstrate the increased robustness of our tracking algorithm.

First published on August 14, 2009
The International Journal of Robotics Research 2009, doi:10.1177/0278364909345167


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