Most existing trackers based on correlation filtering try to introduce different regularization items to improve the learning process of the tracking object. However, the parameters of the corresponding regularization items need to be adjusted dynamically to meet the corresponding tracking process. The regularization parameters are often fixed, so they cannot be updated for more accurate tracking. We propose a method to adaptively adjust the spatiotemporal regularization parameters during the tracking process. We make use of the important correlation between the response map and tracking quality in the tracking process of correlation filtering and introduce the confidence description index of the response map to optimize the spatiotemporal regularization parameters, so the training of correlation filtering can focus on the part with high confidence. In this way, the overfitting situation in training is reduced, and the accuracy and robustness of the tracker are enhanced. In performance evaluation experiments on the OTB2015 and DTB70 datasets, our algorithm outperforms the other comparative algorithms in terms of tracking success rate and distance accuracy, especially in complex scenarios, such as illumination variation, background clutter, out-of-view, and low resolution.