  This paper presents two visual trackers from the different paradigms of
learning and registration based tracking and evaluates their application in
image based visual servoing. They can track object motion with four degrees of
freedom (DoF) which, as we will show here, is sufficient for many fine
manipulation tasks. One of these trackers is a newly developed learning based
tracker that relies on learning discriminative correlation filters while the
other is a refinement of a recent 8 DoF RANSAC based tracker adapted with a new
appearance model for tracking 4 DoF motion.
  Both trackers are shown to provide superior performance to several state of
the art trackers on an existing dataset for manipulation tasks. Further, a new
dataset with challenging sequences for fine manipulation tasks captured from
robot mounted eye-in-hand (EIH) cameras is also presented. These sequences have
a variety of challenges encountered during real tasks including jittery camera
movement, motion blur, drastic scale changes and partial occlusions.
Quantitative and qualitative results on these sequences are used to show that
these two trackers are robust to failures while providing high precision that
makes them suitable for such fine manipulation tasks.
