Abstract: The affine transform is employed to solve the scale change and rotation in in compressed sensing tracking. Firstly according to the Gauss distribution of a certain variance, a number of candidates are produced based on the former tracking result. Then the patch is transformed into the orthogonal coordinate via affine transformation. And the high-dimension feature vector is acquired through the multiple-scale filter. The high-dimension feature vector is compressed through the compressive matrix. Finally, the low-dimension feature vector is passed through the bayes classifier and the candidate with the highest response is recognized as the tracking result, on the basis of which the positive and negative samples are extracted to update the parameters of the naive bayes classifier. Experimental results show that the proposed algorithm can well cope with the scale variation and rotation in compressed visual tracking.

Key words: visual tracking, affine transform, scale variation, object rotation, compressed sensing