Focusing on the issue that traditional correlation filters have poor performance in infrared target tracking with occlusion, an anti-occlusion and real-time target-tracking algorithm based on a multi-scale filter tracker and a deep learning detector is proposed. First, the peak response value is calculated using the tracker; if the peak value is less than the threshold, the target is occluded or tracking is lost. Second, the detector stops updating when the target is occluded or tracking is lost. The position of the target changes significantly when it comes in frame again after occlusion, and the speed of target searching with the tracker will be very slow. At this time, a detector is employed to detect the targets in the subsequent frames without loss of tracking accuracy and speed. The peak values are calculated for each target box that is detected by the detector, and the target with a maximum peak value larger than the threshold is tracked. The results of the experiment compared with the multi-scale correlation filter show that the proposed real-time tracking algorithm can not only effectively solve infrared target occlusion, but also has higher tracking robustness and accuracy.