In order to solve the problem of tracking failure of the Kernelized Correlation Filtering (KCF) tracking algorithm in the case of target scale changes and severe occlusion,an adaptive tracking algorithm is proposed based on kernelized correlation filtering.The algorithm uses a scale estimation strategy to adapt the tracking frame to target scale changes,and uses polynomial kernel functions to reduce the computational complexity.The FHog target feature is used to replace the original Hog feature to obtain more target feature information.In the experiment,50 sets of video sequences based on the OTB-2013 evaluation benchmark were tested and compared with other 31 tracking algorithms to verify the effectiveness of the proposed algorithm.The experimental results show that:the success rate of this algorithm is 0.549 and the accuracy is 0.736,ranking first,which is improved by 3.8% and 1.0% respectively compared with the KCF algorithm.The algorithm has strong steadiness and robustness under complex conditions such as target scale changes and severe occlusion.