  Deep learning has recently started being applied to visual tracking of
generic objects in video streams. For the purposes of robotics applications, it
is very important for a target tracker to recover its track if it is lost due
to heavy or prolonged occlusions or motion blur of the target. We present a
real-time approach which fuses a generic target tracker and object detection
module with a target re-identification module. Our work focuses on improving
the performance of Convolutional Recurrent Neural Network-based object trackers
in cases where the object of interest belongs to the category of
\emph{familiar} objects. Our proposed approach is sufficiently lightweight to
track objects at 85-90 FPS while attaining competitive results on challenging
benchmarks.
