Robot Person Following in Uniform Crowd Environment

TL;DR

This paper introduces a robust RGB-D transformer-based tracker designed for person following in uniform crowd environments, addressing challenges where traditional trackers struggle.

Contribution

The paper presents a novel RGB-D tracker with transformer architecture that improves person tracking in uniform crowds, outperforming existing state-of-the-art methods.

Findings

Higher performance in quantitative metrics

Superior robustness in uniform crowd scenarios

Effective discrimination of target from distractors

Abstract

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.