The traditional Kernel Correlation Filter (KCF) tracking algorithm cannot handle well when the target is moving fast or has large-area occlusionwhich may cause the target to be lost. Based on the traditional KCF algorithmthis paper proposes three mechanismsnamelytarget loss detectionfirst frame re-detection and extended area re-detectionto solve the above problems.The maximum response score and Average Peak Correlation Energy (APCE)are used to determine whether the target is missing.When the target is about to be lostthe extended area re-detection mechanism is adopted. When the target image is similar to the first-frame image of the targetthe first frame re-detection mechanism is adopted. In order to reflect the tracking performance of the proposed algorithm14 sets of video sequences were selected from the VOT2016 and OTB100 data sets as the test setsin which 7 sets of video sequences had the scenarios of target occlusion and fast motion.A quantitative comparative experiment shows thatcompared with the traditional KCF algorithmthe improved algorithm reduces the average Center Position Error (CPE) by 20 pixelsand increases the average Overlapping Rate (OR) by 16.1%.