Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …

Most existing trackers based on discriminative correlation filters (DCF) try to introduce
predefined regularization term to improve the learning of target objects, eg, by suppressing …

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …

Traditional framework of discriminative correlation filters (DCF) is often subject to undesired
boundary effects. Several approaches to enlarge search regions have been already …

Aerial object tracking has recently shown great potential in the field of remote sensing.
However, small objects with limited feature information pose a huge challenge to aerial …

Aerial tracking has received growing attention due to its broad practical applications.
However, single-view aerial trackers are still limited by challenges such as severe …

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based
trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking …

Spatial regularization has been proved as an effective method for alleviating the boundary
effect and boosting the performance of a discriminative correlation filter (DCF) in aerial …

In the domain of visual tracking, most deep learning-based trackers highlight the accuracy
but casting aside efficiency. Therefore, their real-world deployment on mobile platforms like …

Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV)
with high efficiency. However, it has two imperfections, ie, boundary effect and filter …