For the correlation filtering tracking algorithm is not robust enough and cannot adapt to scale changes due to the boundary effect

an improved correlation filtering tracking algorithm based on double model is proposed. Methods: The target tracking consists of position prediction and scale prediction. In the stage of the position prediction

the samples were enhanced to make the samples more consistent with the actual scene.Then the solution is obtained by using the alternating?direction?method?of?multipliers(ADMM)

and the estimated target position is achieved. For scale prediction

a multi-scale pyramid was constructed in order to train the scale filter

and then the target scale is acquired. The final tracking result is decided by target position and scale together. Finally

an occlusion criterion is introduced to determine whether the model is updated or not. Results: Comparing with the classical correlation filtering tracking algorithm

the proposed algorithm boosts the tracking success rate by 18% and tracking accuracy by 11%. Conclusion: Our algorithm in this paper could track the target stably even when the target is occluded and its scale changes.