A Mutli-feature Correlation Filter Tracker with Different Hash Algorithm

Abstract

The discriminative correlation filter (DCF) does not work well in complex tracking scenarios. In order to improve the accuracy of object tracking, a new correlation filter tracker is proposed. We use the different hash algorithm to screen candidate samples, reduce the number of negative samples and improve the speed and accuracy of object tracking; combine the HOG feature with color histogram feature to acquire a robust object appearance model; design an adaptive fusion function to fuse the two features to obtain a more discriminative feature and improve the discriminability of the filter. Experiments on OTB2015 show that the proposed tracker has good accuracy in complex tracking scenes such as fast motion, background clutter, illumination variation, scale variation, etc.