  Computer vision has received a significant attention in recent year, which is
one of the important parts for robots to obtain information about the external
environment. Visual trackers can provide the necessary physical and
environmental parameters for the mobile robot, and their performance is related
to the actual application of the robot. This study provides a comprehensive
survey on visual trackers. Following a brief introduction, we first analyzed
the basic framework and difficulties of visual trackers. Then the structure of
generative and discriminative methods is introduced, and summarized the feature
descriptors, modeling methods, and learning methods which be used in tracker.
Later we reviewed and evaluated the state-of-the-art progress on discriminative
trackers from three directions: correlation filter, deep learning and
convolutional features. Finally, we analyzed the research direction of visual
tracker used in mobile robot, as well as outlined the future trends for visual
tracker on mobile robot.
