  Recently, correlation filter has been widely applied in unmanned aerial
vehicle (UAV) tracking due to its high frame rates, robustness and low
calculation resources. However, it is fragile because of two inherent defects,
i.e, boundary effect and filter corruption. Some methods by enlarging the
search area can mitigate the boundary effect, yet introducing the undesired
background distractors. Another approaches can alleviate the temporal
degeneration of learned filters by introducing the temporal regularizer, which
depends on the assumption that the filers between consecutive frames should be
coherent. In fact, sometimes the filers at the ($t-1$)th frame is vulnerable to
heavy occlusion from backgrounds, which causes that the assumption does not
hold. To handle them, in this work, we propose a novel $\ell_{1}$
regularization correlation filter with adaptive contextual learning and
keyfilter selection for UAV tracking. Firstly, we adaptively detect the
positions of effective contextual distractors by the aid of the distribution of
local maximum values on the response map of current frame which is generated by
using the previous correlation filter model. Next, we eliminate inconsistent
labels for the tracked target by removing one on each distractor and develop a
new score scheme for each distractor. Then, we can select the keyfilter from
the filters pool by finding the maximal similarity between the target at the
current frame and the target template corresponding to each filter in the
filters pool. Finally, quantitative and qualitative experiments on three
authoritative UAV datasets show that the proposed method is superior to the
state-of-the-art tracking methods based on correlation filter framework.
