  Person-tracking robots have many applications, such as in security, elderly
care, and socializing robots. Such a task is particularly challenging when the
person is moving in a Uniform crowd. Also, despite significant progress of
trackers reported in the literature, state-of-the-art trackers have hardly
addressed person following in such scenarios. In this work, we focus on
improving the perceptivity of a robot for a person following task by developing
a robust and real-time applicable object tracker. We present a new robot person
tracking system with a new RGB-D tracker, Deep Tracking with RGB-D (DTRD) that
is resilient to tricky challenges introduced by the uniform crowd environment.
Our tracker utilizes transformer encoder-decoder architecture with RGB and
depth information to discriminate the target person from similar distractors. A
substantial amount of comprehensive experiments and results demonstrate that
our tracker has higher performance in two quantitative evaluation metrics and
confirms its superiority over other SOTA trackers.
