Aiming at the problem that traditional kernel correlation filter（KCF） algorithm often fails in object fast movement

scale changes and occlusion case

an adaptive target tracking method based on extreme learning machine（ELM） and KCF is proposed. Firstly

according to the target location information of the past time

ELM is used to predict the possible position of the target in current frame. Then

the feature sampling of multi-scale target image is carried out based on the target area at the predicted position

and the final position and optimal scale of the target are determined by KCF. Finally

the updating rate of the model is adaptively changed by calculating the oscillation degree of response map at the target location. The proposed algorithm and 6 state-of-the-art algorithms based on correlation filter are tested on 36 public video sequences

the proposed algorithm achieves the best tracking precision and success rate

and it can effectively handle object occlusions

rapid movement and scale changes. Furthermore

it has important theory research and application value.