Target tracking is a basic function of photoelectric equipment.In order to cope with the impact of fast target movement,complex background interference and occlusion in tracking tasks,an infrared target tracking algorithm using deep learning is proposed in this paper,which is different from traditional generative methods and kernel correlation filtering methods.The input is mapped using a double branch Siamese network into a higher dimensional space of features,and the image blocks in video frames divided by anchors are sent into the "classification" and "regression" branches of the regional proposal network.Then,correlation calculations will be conducted on "classification" branch to evaluate the matching scores between features from the template image and the search image,producing a matrix of scores for every anchor generated.The best anchor is selected after the score evaluation,and the target tracking prediction box is determined after the boundary regression from that anchor with the information of "regression" branch.An infrared single light macro single target tracking algorithm meeting the real time requirements is proposed.This approach can be obtained by training the overall system parameters end to end completely offline,is simple to produce,and has full potential for performance that can be exploited with proper parameter fine tuning of the methodology.