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We first formulate, in a coordinate-invariant and geometrically meaningful way, particle filtering on the affine group that allows for combined state\u2014covariance 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.<\/jats:p>","DOI":"10.1177\/0278364909345167","type":"journal-article","created":{"date-parts":[[2009,8,14]],"date-time":"2009-08-14T20:34:18Z","timestamp":1250282058000},"page":"198-217","source":"Crossref","is-referenced-by-count":37,"title":["Visual Tracking via Particle Filtering on the Affine Group"],"prefix":"10.1177","volume":"29","author":[{"given":"Junghyun","family":"Kwon","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Seoul National                         University, Seoul 151-742, Korea,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank C.","family":"Park","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Seoul National                         University, Seoul 151-742, 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