  RGBD object tracking is gaining momentum in computer vision research thanks
to the development of depth sensors. Although numerous RGBD trackers have been
proposed with promising performance, an in-depth review for comprehensive
understanding of this area is lacking. In this paper, we firstly review RGBD
object trackers from different perspectives, including RGBD fusion, depth
usage, and tracking framework. Then, we summarize the existing datasets and the
evaluation metrics. We benchmark a representative set of RGBD trackers, and
give detailed analyses based on their performances. Particularly, we are the
first to provide depth quality evaluation and analysis of tracking results in
depth-friendly scenarios in RGBD tracking. For long-term settings in most RGBD
tracking videos, we give an analysis of trackers' performance on handling
target disappearance. To enable better understanding of RGBD trackers, we
propose robustness evaluation against input perturbations. Finally, we
summarize the challenges and provide open directions for this community. All
resources are publicly available at
https://github.com/memoryunreal/RGBD-tracking-review.
