This paper presents a computer-assisted rapid trajectory extraction technique for soccer robots’ global vision system, focusing on Low-Rank Trajectory Recovery (LRTR). This system is crucial for providing robot posture data, enhancing decision-making, robot trajectories, and maneuver strategies. Given the competition rules demanding quick onsite vision system setup and adjustment, this method offers a speedy and accurate extracction process. It aims to refine trajectory tracking precision in high-level soccer matches, especially by processing varied noisy images in real scenarios. The study delves into theoretical and practical aspects, highlighting its effectiveness in minimizing extraction errors amid the unpredictability of robot movement in competitions. Utilizing robust subspace learning within LRTR significantly enhances tracking accuracy, object recognition, and scene understanding. The innovative trajectory feature extraction method evaluated here shows considerable promise in efficacy and adaptability. The findings advance computer vision system development and improve trajectory interpretation for diverse applications, including sports tracking and autonomous systems. When compared to other algorithms, this method stands out for its extraction precision and efficiency in robot operation, achieving an impressive extraction accuracy of ±2.67 mm without specialized targets, showcasing superior performance.