基于云台的陪伴机器人持续学习人体跟踪
Gimbal-Based Human Tracking for Companion Robots Using Continual Learning
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中文总结 AI 辅助
该研究针对陪伴机器人的人体跟踪问题，提出基于云台摄像头的方法并结合持续学习的ReID策略，提升了跟踪稳定性与连续性，无需可穿戴标签且支持实时重识别。
中文摘要 AI 辅助
可靠且连续的人体跟踪对于自然的人机交互至关重要，尤其针对陪伴机器人。然而，现有许多方法依赖可穿戴标签或视场有限的固定摄像头，这降低了系统灵活性，且当目标移出感知范围时常导致跟踪失败。本文提出一种基于云台摄像头的人体跟踪方法，该摄像头集成于移动机器人上。通过主动控制云台机构，摄像头可动态调整视场方向，即使在机器人与人体发生大幅相对运动时，也能将目标保持在视场内。此外，针对行人重识别（ReID）任务应用持续学习策略，以适应长期跟踪过程中外观变化与环境条件变化。实验结果表明，所提系统显著提升了人体跟踪的稳定性与连续性，支持实时重识别，并能提供响应式反馈，可靠跟踪从步行到跑步的人体运动。用户研究进一步显示，所提方法无需使用可穿戴标签，可提升用户舒适度。
英文摘要
Reliable and continuous human tracking is essential for natural human-robot interaction, particularly for companion robots. However, many existing approaches rely on wearable tags or fixed cameras with limited fields of view, which reduces system flexibility and often causes tracking failures when the target moves outside the sensing range. In this paper, we present a human tracking approach based on a gimbal-mounted camera integrated into a mobile robot. By actively controlling the gimbal mechanism, the camera can dynamically adjust its viewing direction to maintain the target within the field of view, even under substantial relative motion between the robot and the human. Furthermore, a continual learning strategy is applied to the person re-identification (ReID) task to adapt to changes in appearance and environmental conditions during long-term tracking. Experimental results demonstrate that the proposed system significantly improves the stability and continuity of human tracking, enables real-time re-identification, and provides responsive feedback for reliable tracking of human motion from walking to running. User studies further indicate that the proposed approach enhances user comfort by eliminating the need for wearable tags.
发表机构
- National Cheng Kung University (NCKU)（成功大学）
机构由 AI 辅助整理，请以论文原文为准。
