The architecture of the trackinglearningdetection (TLD) algorithm is a good reference  for studying longtime single object tracking algorithms. However, due to some defects of its own, the TLD algorithm tends to cause the accumulation of errors and the occurrence of losing objects in complex situations such as fast moving, occlusion and light changes. Given the limitation of the median flow algorithm as the tracker in the tracking module of the TLD algorithm, we propose a TLD object tracking algorithm based on KCF similarity (TLD-KCFS). The KCF algorithm is used to monitor the TLD tracking in real time. The similarity is calculated via the tracking results to judge the switching of the detection module, and the bounding box is adjusted by the combination of the two results. Tests on several different types of videos show that the TLD-KCFS algorithm can achieve stable and good tracking output in complex situations such as blur, fast moving, occlusion, and light changes. It is robust and suitable for longtime object tracking.