From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction

• URL: http://arxiv.org/abs/2504.06357v1
• Date: Tue, 08 Apr 2025 18:10:44 GMT
• Title: From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction
• Authors: Vladimir Golovkin, Nikolay Nemtsev, Vasyl Shandyba, Oleg Udin, Nikita Kasatkin, Pavel Kononov, Anton Afanasiev, Sergey Ulasen, Andrei Boiarov,
• Abstract summary: Game State Reconstruction (GSR) involves precise tracking and localization of all individuals on the football field-players, goalkeepers, referees, and others.<n>This capability enables coaches and analysts to derive actionable insights into player movements, team formations, and game dynamics.<n>We present a robust end-to-end pipeline for tracking players across an entire match using a single-camera setup.
• Score: 0.19748373512880277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Game State Reconstruction (GSR), a critical task in Sports Video Understanding, involves precise tracking and localization of all individuals on the football field-players, goalkeepers, referees, and others - in real-world coordinates. This capability enables coaches and analysts to derive actionable insights into player movements, team formations, and game dynamics, ultimately optimizing training strategies and enhancing competitive advantage. Achieving accurate GSR using a single-camera setup is highly challenging due to frequent camera movements, occlusions, and dynamic scene content. In this work, we present a robust end-to-end pipeline for tracking players across an entire match using a single-camera setup. Our solution integrates a fine-tuned YOLOv5m for object detection, a SegFormer-based camera parameter estimator, and a DeepSORT-based tracking framework enhanced with re-identification, orientation prediction, and jersey number recognition. By ensuring both spatial accuracy and temporal consistency, our method delivers state-of-the-art game state reconstruction, securing first place in the SoccerNet Game State Reconstruction Challenge 2024 and significantly outperforming competing methods.

Related papers

• WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation [67.28831601491447]
  WorldPose is a novel dataset for advancing research in multi-person global pose estimation in the wild.<n>We exploit the static multi-view setup of HD cameras to recover the 3D player poses and motions with unprecedented accuracy.<n>The resulting dataset comprises more than 80 sequences with approx 2.5 million 3D poses and a total traveling distance of over 120 km.
  arXiv  Detail & Related papers  (2025-01-06T05:24:38Z)
• BroadTrack: Broadcast Camera Tracking for Soccer [6.011159943695013]
  Camera calibration and localization enables many applications in the context of soccer broadcasting.<n>We present a system capable of addressing the task of soccer broadcast camera tracking efficiently, robustly, and accurately.<n>Our tracking system, BroadTrack, halves the mean reprojection error rate and gains more than 15% in terms of Jaccard index for camera calibration on the SoccerNet dataset.
  arXiv  Detail & Related papers  (2024-12-02T17:10:52Z)
• SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap [102.5232204867158]
  We formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration. Our experiments show that GSR is a challenging novel task, which opens the field for future research.
  arXiv  Detail & Related papers  (2024-04-17T12:53:45Z)
• Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera [15.032833555418314]
  We present two novel end-to-end computer vision frameworks designed specifically for setting strategy classification in volleyball matches from a single camera view. Our frameworks combine setting ball trajectory recognition with a novel set trajectory classifier to generate comprehensive and advanced statistical data. Our system allows for real-time deployment, enabling in-game strategy analysis and on-the-spot gameplan adjustments.
  arXiv  Detail & Related papers  (2023-09-26T08:29:02Z)
• Graph-Based Multi-Camera Soccer Player Tracker [1.6244541005112743]
  The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions. Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance.
  arXiv  Detail & Related papers  (2022-11-03T20:01:48Z)
• Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking [58.95210121654722]
  We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
  arXiv  Detail & Related papers  (2022-04-15T12:47:01Z)
• SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos [62.686484228479095]
  We propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each. The dataset is fully annotated with bounding boxes and tracklet IDs. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved.
  arXiv  Detail & Related papers  (2022-04-14T12:22:12Z)
• Smart Director: An Event-Driven Directing System for Live Broadcasting [110.30675947733167]
  Smart Director aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time. Our system is the first end-to-end automated directing system for multi-camera sports broadcasting.
  arXiv  Detail & Related papers  (2022-01-11T16:14:41Z)
• Real-Time Camera Pose Estimation for Sports Fields [43.50671336931042]
  We propose a novel framework that combines accurate localization and robust identification of specific keypoints in the image. Our algorithm exploits both the field lines and the players' image locations, assuming their ground plane positions to be given. We will demonstrate its effectiveness on challenging soccer, basketball, and volleyball benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-31T11:27:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.