Visual object tracking has attracted widespread interests recently. Due to the complementary features provided by visible and infrared images, fusion tracking based on visible and infrared images can boost the tracking performance under adverse challenging conditions. RGB-infrared fusion tracking has become an active research topic and various algorithms have been proposed in recent years. In this paper, we present a review on RGB-infrared fusion tracking. We summarize all major RGB-infrared trackers in the literature and categorize them into several major groups for better understanding. We also discuss the development of RGB-infrared datasets, and analyze the main results on public datasets. We observe that deep learning-based methodsachieve the state-of-the-art performances. Besides, the graph-based and correlation filter-based methods give a bit worse but still competitive performances. In conclusion, we give some suggestions on future research directions of fusion tracking based on our observations. This review can serve as a reference for researchers in RGB-infrared fusion tracking, image fusion, and related fields.