  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
did not 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 have proven the superiority of our
method compared to the state-of-the-art CPU- and GPU-based trackers, with a
speed of ~60 frames per second running on a single CPU.
  Our tracker is additionally proposed to be applied in UAV localization.
Considerable tests in the indoor practical scenarios have proven the
effectiveness and versatility of our localization method. The code is available
at https://github.com/vision4robotics/AutoTrack.
