Human Following in Mobile Platforms with Person Re-Identification

TL;DR

This paper introduces a comprehensive human-following system for mobile robots that integrates a novel person re-identification module with motion prediction and adaptive following strategies, improving robustness in complex real-world scenarios.

Contribution

The paper presents a new person re-identification module combining visual registration, neural face and torso recognition, and motion prediction, enhancing human-following capabilities in mobile platforms.

Findings

Significant improvement over baseline methods in re-identification accuracy.

Effective handling of occlusions and fast-moving targets.

Robust performance in real-world, crowded environments.

Abstract

Human following is a crucial feature of human-robot interaction, yet it poses numerous challenges to mobile agents in real-world scenarios. Some major hurdles are that the target person may be in a crowd, obstructed by others, or facing away from the agent. To tackle these challenges, we present a novel person re-identification module composed of three parts: a 360-degree visual registration, a neural-based person re-identification using human faces and torsos, and a motion tracker that records and predicts the target person's future position. Our human-following system also addresses other challenges, including identifying fast-moving targets with low latency, searching for targets that move out of the camera's sight, collision avoidance, and adaptively choosing different following mechanisms based on the distance between the target person and the mobile agent. Extensive experiments show that our proposed person re-identification module significantly enhances the human-following feature compared to other baseline variants.