Gimbal-Based Human Tracking for Companion Robots Using Continual Learning
For companion robot developers, this work offers a practical solution to improve human tracking robustness and user comfort, but it is an incremental improvement over existing tracking methods.
The paper presents a gimbal-based human tracking system for companion robots that actively adjusts the camera view to keep the target in sight and uses continual learning for person re-identification. Experiments show improved tracking stability and continuity, enabling real-time re-identification from walking to running, and user studies indicate enhanced comfort by removing wearable tags.
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.