Aberrance Learning via Time-driven Correlation Filter for Object Tracking

In order to solve the problem that the traditional correlation filter tracking algorithm is easy to fail in complex environments, this paper proposes time-driven correlation filter with aberrance learning (ALTCF) to improve the adaptability of the model in complex environments and achieve safe and effective object tracking. By introducing temporal regularization term with aberrance learning, the model in this paper can not only search for objects by combining the similarity of filter response maps and temporal features to achieve the effect of suppressing aberrance, but also improve the robustness of appearance model and alleviate temporal filter degradation. In addition, this paper uses the alternating direction method of multipliers (ADMM) algorithm to achieve the optimization process of the model, which greatly reduces the computational complexity of the model. A large number of experiments confirm the superiority of ALTCF tracking performance.