The tracking algorithm based on deep-level siamese network generally lacks the capability to update the target template online; hence, it exhibits poor adaptability in some complex application environments. Aiming to resolve this problem, a target template online-updating algorithm based on optical flow mapping is proposed herein. On the premise of ensuring real-time operation, the proposed algorithm can efficiently improve its adaptability in complex circumstances. First, the optical-flow information between the template frames is calculated in the tracking process. Then, the information of motion change is generated via optical flow mapping and residual calculation. Furthermore, based on singular value decomposition, a method that creates a correction term via the initial frame, which modifies the target-position deviation, is proposed herein. Finally, the proposed algorithm is tested on OTB100 and VOT2016 datasets. The results show that the proposed algorithm can optimize the new target template to enhance the robustness and can achieve the best results compared with existing tracking algorithms.