Robust visual tracking based on kernelized correlation filters

Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of the tracking method. Two robust loss functions are offered to address the problem of overfitting in the training process. In addition, a separate scale filter is learned for scale estimation during the tracking process. A large number of experimental results in a variety of complex environments demonstrate that the robustness of visual tracking based on kernelized correlation filters has been greatly improved by our robust collaborative regularization correlation model.