Kernelized correlation filters (KCF) algorithm tracker updating its model parameters at every frame makes it unable to effectively deal with the problems of fast motion and interference of the target in most environments. A nuclear-related object tracking method based on enhanced threshold updating is proposed. Based on the average peak correlation energy (APCE), the APCE threshold and APCE gradient threshold are combined to determine the reliability of the tracking results, which are used to determine whether the model is updated. In this paper, the APCE threshold value is reversely enhanced and the APCE gradient threshold is positively strengthened. When the APCE and APCE gradients are all higher than the respective thresholds, the model is updated, otherwise, the model will stop updating. The quantitative and qualitative experiments show that the algorithm is more effective than the KCF algorithm for the fast motion and interference of the target. The proposed algorithm also provides a good reference value for the design of tracking algorithm based on the idea of gradient detection tracking performance and threshold enhancement.