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Recently, correlation filter has been widely applied in unmanned aerialvehicle (UAV) tracking due to its high frame rates, robustness and lowcalculation resources. However, it is fragile because of two inherent defects,i.e, boundary effect and filter corruption. Some methods by enlarging thesearch area can mitigate the boundary effect, yet introducing the undesiredbackground distractors. Another approaches can alleviate the temporaldegeneration of learned filters by introducing the temporal regularizer, whichdepends on the assumption that the filers between consecutive frames should becoherent. In fact, sometimes the filers at the ($t-1$)th frame is vulnerable toheavy occlusion from backgrounds, which causes that the assumption does nothold. To handle them, in this work, we propose a novel $\ell_{1}$regularization correlation filter with adaptive contextual learning andkeyfilter selection for UAV tracking. Firstly, we adaptively detect thepositions of effective contextual distractors by the aid of the distribution oflocal maximum values on the response map of current frame which is generated byusing the previous correlation filter model. Next, we eliminate inconsistentlabels for the tracked target by removing one on each distractor and develop anew score scheme for each distractor. Then, we can select the keyfilter fromthe filters pool by finding the maximal similarity between the target at thecurrent frame and the target template corresponding to each filter in thefilters pool. Finally, quantitative and qualitative experiments on threeauthoritative UAV datasets show that the proposed method is superior to thestate-of-the-art tracking methods based on correlation filter framework.

Sparse Regularized Correlation Filter for UAV Object Tracking with adaptive Contextual Learning and Keyfilter Selection