Correlation Lilter Tracking Algorithm Focusing on Learning Spatio-Temporal Relationship

Traditional correlation filter tracking algorithms try to introduce predefined regularization terms, such as restraining background learning or limiting the learning rate of correlation filter, to improve the robustness of the algorithm. However, in complex scenes, target tracking loss is easy to occur, because the traditional correlation filter tracking algorithm does not pay attention to the information changes between two adjacent frames. In order to solve the above problems, this paper proposes a correlation filter tracking algorithm focusing on learning the spatial-temporal relationship. The change of the response graph of two adjacent frames is introduced as the reference weight of the weight value of the spatial regularization term, and the oscillation degree of the response graph of the current frame determines the weight value of the temporal regularization term. Experiments on OTB-50, OTB-100 and OTB-2013 benchmark datasets show that the proposed algorithm is more robust in complex scenes.