Abstract: With the development of deep learning, many RGB object tracking algorithms based on deep learning have been proposed with promising performance in recent years. However, algorithms that rely solely on visible image for tracking make it difficult to achieve robust tracking in difficult scenarios such as illumination variation and full occlusion. To solve the challenges in high difficulty scenarios and achieve efficient and robust target tracking, multimodal target tracking has emerged. This paper focus on the RGB-D tracking method. The RGB-Depth multimodal tracking methods in recent years are listed in detail, and the advantages and disadvantages of each method are analyzed and compared. Secondly, we will introduce the mainstream RGB-D tracking datasets and their evaluation indicators. Finally, we summarize the development trends and challenges in RGB-D object tracking technology and looks forward to future directions, including the construction of specialized scenario RGB-D datasets, the establishment of novel RGB-D object tracking evaluation indicators, and the development of effective modal fusion paradigms for RGB-D models.