In recent years, siamese tracking networks have achieved promising performance in visual tracking. However, there is still large room for improvement in the challenge of target state estimation and complex aberrances for siamese trackers. With the success of deep learning in object detection, more and more object detection technologies are used to guide object tracking. This survey reviews the siamese tracking algorithms integrating detection technologies. Firstly, we introduce the relation and difference between detection and tracking, and analyze the feasibility of improving siamese tracking algorithms by detection technologies. Then, we elaborate the existing siamese trackers based on different detection frameworks. Furthermore, we conduct extensive experiments to compare and analyze the representative methods on the popular OTB100, VOT2018, GOT-10k, and LaSOT benchmarks. Finally, we summarize our manuscript and prospect the further trends of visual tracking.