Correlation Filters with Limited Boundaries

Correlation filters take advantage of specific proper-ties in the Fourier domain allowing them to be estimated efficiently: O(ND logD) in the frequency domain, ver-sus O(D3 + ND2) spatially where D is signal length, and N is the number of signals. Recent extensions to cor-relation filters, such as MOSSE, have reignited interest of their use in the vision community due to their robustness and attractive computational properties. In this paper we demonstrate, however, that this computational efficiency comes at a cost. Specifically, we demonstrate that only 1 D proportion of shifted examples are unaffected by boundary effects which has a dramatic effect on detection/tracking performance. In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency do-main, (ii) dramatically reduces boundary effects, and (iii) is able to implicitly exploit all possible patches densely ex-tracted from training examples during learning process. Im-pressive object tracking and detection results are presented in terms of both accuracy and computational efficiency. 1