Robot Person Following in Uniform Crowd Environment
This work addresses a challenging scenario for robots in applications like security and elderly care, but it is incremental as it builds on existing tracking methods.
The paper tackles the problem of person-following robots in uniform crowd environments by developing a new RGB-D tracker, DTRD, which achieves higher performance in two quantitative metrics compared to state-of-the-art trackers.
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.