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Online improved eigen tracking
S. Tripathi, , S.D. Roy
Published in
Pages: 278 - 281
We present a novel predictive statistical framework to improve the performance of an Eigen Tracker which uses fast and efficient eigen space updates to learn new views of the object being tracked on the fly using candid co-variance free incremental PCA. The proposed system detects and tracks an object in the scene by learning the appearance model of the object online motivated by non-traditional uniform norm. It speeds up the tracker many fold by avoiding nonlinear optimization generally used in the literature. © 2009 IEEE.
About the journal
JournalProceedings of the 7th International Conference on Advances in Pattern Recognition, ICAPR 2009