SoccerNet 2025 Challenges Results

The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understanding. This year's challenges span four vision-based tasks: (1) Team Ball Action Spotting, focused on detecting ball-related actions in football broadcasts and assigning actions to teams; (2) Monocular Depth Estimation, targeting the recovery of scene geometry from single-camera broadcast clips through relative depth estimation for each pixel; (3) Multi-View Foul Recognition, requiring the analysis of multiple synchronized camera views to classify fouls and their severity; and (4) Game State Reconstruction, aimed at localizing and identifying all players from a broadcast video to reconstruct the game state on a 2D top-view of the field. Across all tasks, participants were provided with large-scale annotated datasets, unified evaluation protocols, and strong baselines as starting points. This report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community. The SoccerNet Challenges continue to serve as a driving force for reproducible, open research at the intersection of computer vision, artificial intelligence, and sports. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet.

A Reinforcement Learning Based Motion Planner for Quadrotor Autonomous Flight in Dense Environment

Quadrotor motion planning is critical for autonomous flight in complex environments, such as rescue operations. Traditional methods often employ trajectory generation optimization and passive time allocation strategies, which can limit the exploitation of the quadrotor's dynamic capabilities and introduce delays and inaccuracies. To address these challenges, we propose a novel motion planning framework that integrates visibility path searching and reinforcement learning (RL) motion generation. Our method constructs collision-free paths using heuristic search and visibility graphs, which are then refined by an RL policy to generate low-level motion commands. We validate our approach in simulated indoor environments, demonstrating better performance than traditional methods in terms of time span.