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Abstract：To address the problems that traditional kernel correlation filtering (KCF) cannot effectively use color features in tracking color video sequences and has low ability to deal with target occlusion and deformation, a multi-feature fusion kernel correlation filtering tracking algorithm with response confidence was proposed. The algorithm first extracts the orientation and color histogram features of the target image, to determine the tracking of the target by calculating the percentage of high response value points in the upper layer of the response map, and then adjusts the size of the learning rate; then the product of the average peak correlation energy (APCE) and the maximum response peak of the two features is used to weigh the fusion target positions. The experimental comparison shows that the tracking algorithm proposed improves 12.8% and 22.6% respectively in accuracy and success rate compared with the KCF algorithm, and still has strong robustness under complex situations such as occlusion, fast motion and rotation of the target.