Abstract: With the rapid development of the unmanned aerial vehicle (UAV) industry, the dramatic increase in aerial imagery data has made intelligent analysis and processing of aerial images a new research focus. Object tracking, as one of the core technologies, provides fundamental support for further imagery content understanding and various practical applications. Affected by various factors such as complex application scenarios, frequent changes in target scale, target posture change, and similar target interference, object tracking in UAV imagery faces many technical challenges. The main techniques of single object tracking in UAV imagery in recent years, including object tracking methods based on correlation filter, deep learning, as well as combination of correlation filter and deep learning, were summarized and the public datasets of UAV imagery and evaluation metrics for object tracking performance were discussed. Then, the performance evaluation and analysis of typical single object tracking methods were performed. Finally, the future development tendency of object tracking in UAV imagery was summarized and prospected.