The traditional Kernel Correlation Filtering (KCF) tracking algorithm lacks the ability to deal with target occlusion.To solve the problem,occlusion judgment indexes and an improved algorithm with adaptive model updating are proposed.Two indexes,maximum response value and the number of low response points,are used to comprehensively determine whether there is occlusion,and then the models learning rate is adjusted adaptively,so as to avoid inaccurate tracking in the presence of occlusion.The image sequences with occlusion are selected from OTB2015 data set to verify the performance of the algorithm.Compared with that of the traditional KCF algorithm for tracking under occlusion,the accuracy is increased by 15.12%,and the success rate is increased by 14.7%.The experimental results show that the improved algorithm can accurately track targets when there is occlusion with higher accuracy and stronger robustness.