Support Correlation Filters Tracking using Mask Matrix

Support correlation filter tracking method uses cyclic sampling to transform the calculation into frequency domain, which solves the problems of sampling and large computation of support vector machine. However, the current method can not exploit the information of backgrounds because all samples are generated by cyclic sampling around the target in the tracking process. To solve this problem, this paper proposes a background awareness support correlation filter tracking method using mask matrix. In the tracking process, the mask matrix is used to extract the patchs densely from background as negative samples, so the background information is used effectively. Experiments on OTB100 database show that compared with Scale Kerneling Supported Correlation Filtering (SKSCF), the proposed algorithm achieves a gain of 4.2% in mean OP and 6.2% AUC score respectively.