Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer didn’t think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks, i.e., DTB70, UAVDT, UAV123@10fps and VisDrone2018, have proven that our tracker performs favorably against the state-of-the-art CPU- and GPU-based trackers, with average speed of 60.9 frames per second (FPS) running on a single CPU.
Our tracker is additionally proposed to be applied to localize the moving camera. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method.