Single object tracking is an important research direction in the field of computer vision， and has a wide range of applications in video surveillance， autonomous driving and other fields. For single object tracking algorithms， although a large number of summaries have been conducted， most of them are based on correlation filter or deep learning. In recent years， Siamese network-based tracking algorithms have received extensive attention from researchers for their balance between accuracy and speed， but there are relatively few summaries of this type of algorithms and it lacks systematic analysis of the algorithms at the architectural level. In order to deeply understand the single object tracking algorithms based on Siamese network， a large number of related literatures were organized and analyzed. Firstly， the structures and applications of the Siamese network were expounded， and each tracking algorithm was introduced according to the composition classification of the Siamese tracking algorithm architectures. Then， the commonly used datasets and evaluation metrics in the field of single object tracking were listed， the overall and each attribute performance of 25 mainstream tracking algorithms was compared and analyzed on OTB 2015 （Object Tracking Benchmark） dataset， and the performance and the reasoning speed of 23 Siamese network-based tracking algorithms on LaSOT （Large-scale Single Object Tracking） and GOT-10K （Generic Object Tracking） test sets were listed. Finally， the research on Siamese network-based tracking algorithms was summarized， and the possible future research directions of this type of algorithms were prospected.