Single object tracking is a research focus in the field of computer vision. Traditional algorithms including correlation filtering have fast tracking speed but generally low tracking accuracy due to the roughness of extracted manual features such as color and gray levels. With the development of deep learning theory in recent years, tracking methods using deep features can achieve a good balance between tracking accuracy and speed. This study first introduces the relevant background of single object tracking and then sorts out multiple algorithms that have emerged in the development of single object tracking from the two stages of single object tracking based on correlation filters and deep learning. The current mainstream Siamese network algorithms are also introduced in detail. Finally, a large data set is used to compare and analyze the excellent algorithms that have emerged in recent years. In view of the shortcomings and deficiencies of these algorithms, the development prospects of this field are provided in this study.