Abstract:  At present, several mainstream algorithms based on nuclear correlation filters still have target drift or even loss. The algorithm in this article is in fusion and improvement based on the two major algorithms，Staple: Complementary Learners for Real-Time Tracking (hereinafter referred to as Staple) and Large Margin Object Tracking with Circulant Feature Maps ( hereinafter referred to as LMCF). First, the Bayesian formula is used to solve the histogram of the background and foreground of the current frame, and the foreground histogram is equalized to eliminate certain noise; second, the Bhattacharyya Coefficient is used to calculate the histogram of the background and foreground of the current frame for similarity value, the threshold is set through data analysis to reduce the problem that the target and the background are too similar; again, the bin value of the histogram is adapted according to the set threshold to improve the resolution of tracking; then the LMCF algorithm is used to calculate the maximum peak value and average peak correlation energy (APCE) of the current frame, and set an appropriate threshold based on data analysis; finally, the maximum value within the threshold range and APCE value are combined with the fusion coefficient to make the algorithm achieve adaptive fusion. The algorithm in this paper (LMCF-Staple) is tested on the public data set platforms OTB50 and OTB100, the public data set platform. The results show that the tracking stability of the LMCF-Staple algorithm is greatly improved, the target drift is significantly reduced, and its tracking accuracy and success rate are better than the current mainstream algorithms.