Multi-Kernel Correlation Filter for Visual Tracking

Correlation filter based trackers are ranked top in terms of performances. Nevertheless, they only employ a single kernel at a time. In this paper, we will derive a multi-kernel correlation filter (MKCF) based tracker which fully takes advantage of the invariance-discriminative power spectrums of various features to further improve the performance. Moreover, it may easily introduce location and representation errors to search several discrete scales for the proper one of the object bounding box, because normally the discrete candidate scales are determined and the corresponding feature pyramid are generated ahead of searching. In this paper, we will propose a novel and efficient scale estimation method based on optimal bisection search and fast evaluation of features. Our scale estimation method is the first one that uses the truly minimal number of layers of feature pyramid and avoids constructing the pyramid before searching for proper scales.