AI‐Driven Soccer Training Optimization Method via Space‐Air‐Ground Integrated Network
Debao Liu, Dan Li, Qingbao Wang · Transactions on Emerging Telecommunications Technologies · 2025
ABSTRACT This paper introduces an AI‐driven soccer training optimization method based on the space–air–ground integrated network, named SAGIN‐Play, which integrates real‐time multimodal data from ground sensors, aerial surveillance (drones), and cloud‐based processing. The system is designed to optimize player performance, enhance tactical positioning, and provide real‐time feedback during soccer training and matches. By leveraging wearable motion sensors, drone‐based aerial surveillance, and cloud computing, the method enables precise tracking of player actions and interactions, facilitating personalized performance improvements. This paper evaluates the proposed method across various datasets, including SoccerNet, Kaggle Football, UCF101 Sports, and the player event system (PES), highlighting the effectiveness of SAGIN‐Play in action recognition, tactical positioning, and real‐time feedback precision. The method outperforms traditional techniques, such as object detection models, multi‐object tracking, and reinforcement learning (RL), in key metrics, demonstrating its potential in dynamic soccer training environments. Additionally, an ablation study reveals the critical contributions of each SAGIN‐Play component, particularly aerial surveillance and cloud‐based processing, in optimizing player positioning and tactical execution. The study further demonstrates the system's ability to improve player performance through personalized feedback, showing significant progress in key skill areas like passing, shooting, and defense. Simulated live training sessions demonstrate substantial improvement in coordination, decision‐making, and overall performance. The results underscore the importance of integrating multilayered data for real‐time tactical adjustments in soccer training. Experimental results show that SAGIN‐Play achieves 94.2% action recognition accuracy on SoccerNet and 92.8% tactical positioning accuracy on Kaggle Football, while reducing the average feedback latency from 650 ms to 240 ms compared with baseline models.