A Fast Tracking Network for Pedestrian Following of Mobile Robot in Unknown Complex Scenes

The pedestrian-following robot aims to robustly maintain a standard distance from a specific pedestrian in an unknown complex environment, which is challenging when facing the limited field of view (FOV) and the computing resource constraints. However, the pedestrian-following robot commonly utilizes the long range dependence of targets to construct tracking models via the transformer tracking method, which is often insufficient to achieve real-time following. To address this important issue, we propose a new fast tracking network for the following robot, including a lightweight target detector, a target state prediction decoder, and an adaptive visual servo controller. First, in the lightweight target detector, a dense feature extraction process is designed by stacking depthwise group-separable convolutions. A context encoder is also developed, and is coupled with the proposed person re-identification (Re-ID) branch to detect multiple targets with high precision and speed. Second, in the target state prediction decoder, we introduce a flexible multiattention mechanism to obtain target Re-ID features from the previous frame for predicting the target's position in the current frame. Third, in the adaptive visual servo controller, we design a six-parameter dynamic model for the proportional integral derivative (PID) controller to stably follow the pedestrian under the limited FOV. Extensive experimental results demonstrate that the proposed method is efficient and accurate. Moreover, it also exhibits strong robustness and high real-time performance in unknown complex environments.