Due to the target appearance changes and occlusion during tracking

the KCF algorithm using a single iterative update filter will accumulate much more noise information in the process of learning

which leads to the loss of the target.To solve this problem

we propose a correlation filtering target tracking algorithm based on multi-lifespan learning.We establish complementary relationship among global stage filter model

consistency stage filter model and initial stage filter model to parallel track the target.The experimental results achieved on the 51 video databases on benchmark show that our algorithm is superior to most existing methods in both overall accuracy and overall success rate with the scores of 77.6% and 68.9%

respectively.