A Survey of RGB-Depth Object Tracking

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 light for tracking make it difficult to achieve robust tracking in difficult scenarios such as illumination variation and full occlusion. Therefore, some researchers believe that changing the data modality and quality is a new approach. They integrate the depth, infrared, events and semanteme modal information with RGB data to make the tracker perform better in specific scenarios. This paper will mainly describe 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 trend and challenges of RGB-D target tracking technology and prospects its future development.