Learning Adaptive Spatial-temporal Structured Correlation Filters for UAV Object Tracking

In visual object tracking via unmanned aerial vehicle (UAV), discriminative correlation filtering (DCF) is one of the major methods owing to circulant samples which can be utilized not only for computing economically but also to hasten the optimization of filters. The universal DCF methods are seen as ridge regression models with some kinds of regularizations, which may likely result in tracking drift. Inspired by structured SVM, a generic framework that combines the squared hinge loss with spatial-temporal regularizations is advocated in this letter to distinguish the feature of targets from surrounding backgrounds and thus improve the robustness in tracking. Meanwhile, the standard squared norm penalty is turned into a group lasso penalty in the multichannel framework which enables filters with modest channel selection. The proposed adaptive spatial-temporal structured correlation filtering (ASTSCF) method has attained competitive results on major UAV tracking benchmarks.