A coupling method of learning structured support correlation filters for visual tracking

The correlation filtering method is one of the mainstream methods in the visual target tracking task. One of the reasons is that the introduction of cyclic samples facilitates the calculation of optimizing filters. But usual correlation filtering frameworks which are attributable to a ridge regression model based on least squares error with various regularizations put emphasis on modeling a linear system for samples themselves, so maybe likely result in over-fitting. With the appearance of either target or background varying, the credibility of the response obtained after filtering would be decrease. Some researchers thus have tried to incorporate other models such as SVMs to obtain better robustness. In this study, we propose a natural coupling method called StrucSCF of integrating structured SVM by background awareness into the correlation filtering framework, which put more emphasis on the discrepancy between the target and background samples to enhance the discrimination and robustness of tracking. Meanwhile, for the sake of online updating the filters based on structured SVM with real-time performance, we take advantage of the fast Fourier transform on the circulant samples to speed up solving the structured SVM-based filters. In addition, we extend the StrucSCF method with Laplacian temporal regularization to demonstrate that it has as good quality of extension as the conventional correlation filtering framework. The proposed StrucSCF has achieved competitive performance compared with the baseline and other advanced methods in mainstream benchmarks.