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.

翻译：人类跟随是人机交互中的一项关键功能，但在实际场景中给移动代理带来了诸多挑战。主要难点包括目标人员可能处于人群中、被他人遮挡或背对代理。为解决这些挑战，我们提出一种新型行人再识别模块，该模块包含三个组成部分：360度视觉注册、基于神经网络并利用人脸与躯干特征的行人再识别，以及记录并预测目标人员未来位置的运动追踪器。我们的人体跟随系统还应对其他挑战，包括低延迟识别快速移动目标、搜索移出摄像机视野的目标、碰撞避免，以及根据目标人员与移动代理之间的距离自适应选择不同跟随机制。大量实验表明，与其他基线变体相比，我们提出的行人再识别模块显著增强了人体跟随功能。