Intelligent following control for electric-drive unmanned ground vehicles (UGVs) is important for human–robot collaboration and autonomous mobility. However, target occlusion, detection noise, and short-term detection failures can reduce following stability and reliability. To address these challenges, this paper proposes a vision-based following method that integrates adaptive Kalman filtering with target-state-aware control. The proposed method updates the observation noise covariance online using an innovation-sequence sliding window and evaluates the validity of visual measurements based on the intersection over union between adjacent frames, thereby enabling target position prediction and compensation under unstable observation conditions. Meanwhile, the longitudinal velocity and lateral angular velocity are dynamically adjusted according to the target detection state, image-center offset, and distance variation to achieve continuous and stable following control. Experimental results show that the proposed method reduces the target position prediction error by approximately 10% compared with conventional Kalman filtering and maintains the longitudinal following distance at about 3 m, improving the robustness, continuity, and motion smoothness of electric-drive UGV target following in complex environments.