Boundary effect is an important factor which restricts the performance of correlation filter. At present

most methods simply use the prior knowledge

such as inverse Gaussian distribution

preset masks

etc.

or segment the foreground target to constrain solving as the regularization term

which do not consider characteristics of the target in the spatial and temporal domain. To address this problem

this paper proposes a learning attention regularized correlation filter for visual tracking. The method uses the attention mechanism to learn the specific spatial weight of the target

which can adapt to the variations of target in the spatial domain by considering the spatial distribution characteristics of the target. At the same time

this paper uses the continuity of the target in the temporal domain. The filter is indirectly adjusted by constraining the attention weight matrix. Finally

the alternating direction method of multipliers (ADMM) is employed to iteratively optimize the model. We conduct extensive experiments on the proposed method in the standard tracking database. The results show that the proposed algorithm can track the target in real time

and has a certain improvement in precision and success rate.