The invention discloses a tracking algorithm based on high confidence update supplementary learning, which belongs to the field of image processing. At first, the correlation filter response value isobtained according to the standard correlation filter classifier, and the peak-to-side ratio of the correlation filter response is calculated as a response confidence level; if the confidence level isgreater than an average threshold, the current frame continues to update the correlation filter, and if the confidence level is less than an average threshold, the update of the filter is stopped; then, the number of frames that are not updated continuously is calculated, and if there are 10 consecutive frames that are not updated, the frames are updated forcibly; finally, the total response is obtained by fusing the response of the color supplementary learner, and the position of the maximum value in the response is the tracking result. The invention remarkably improves the robustness of thetracking algorithm, can effectively distinguish the target from the background, further improves the precision of the tracker, and remarkably improves the robustness and accuracy of the tracker underthe condition that the illumination changes drastically and the target scene is complex. The algorithm is free of the influence of the change of the tracking target environment, and can effectively and accurately track the target object.