Robot Person Following in Uniform Crowd Environment
Adarsh Ghimire, Xiaoxiong Zhang, Sajid Javed, Jorge Dias, Naoufel, Werghi

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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Social Robot Interaction and HRI · Human Mobility and Location-Based Analysis
