SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines

• URL: http://arxiv.org/abs/2606.09679v1
• Date: Mon, 08 Jun 2026 15:59:56 GMT
• Title: SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines
• Abstract summary: SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge requires predicting who performs which action and when across eight classes in broadcast soccer.<n>Our system achieves 0.548 Macro F1 on the test set and 0.446 on the challenge set (server evaluation)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe our system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge, which requires predicting who performs which action and when, across eight classes in broadcast soccer. Building on the three FOOTPASS baselines [1] (TAAD, TAAD+GNN, and TAAD+DST), we contribute four extensions: (1) gradient check pointing to enable full-backbone fine-tuning on a single GPU; (2) fusion of GNN logits into the DST encoder, combining graph-based tactical context with per-player visual features; (3) square-root frequency class weighting to address the 213:1 pass-to-tackle imbalance in the training data; and (4) a post processing pipeline comprising per-class logit gating, temporal frame refinement, jersey re-assignment, and a two-model ensemble. Our system achieves 0.548 Macro F1 on the test set and 0.446 on the challenge set (server evaluation).

Related papers

• The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering [196.78334982965507]
  EgoCross is a cross-domain egocentric video question answering benchmark.<n>It is designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios.<n>The first EgoCross Challenge was hosted at the Third EgoVis Workshop at CVPR 2026.
  arXiv  Detail & Related papers  (2026-08-05T08:51:20Z)
• SoccerNet 2026 Challenges Results [146.24850117279183]
  SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open effort, dedicated to advancing computer vision research in sports video understanding.<n>This paper describes each task and its evaluation protocol, presents the challenge leaderboards, and summarizes the leading submissions.
  arXiv  Detail & Related papers  (2026-07-08T12:06:48Z)
• SoccerNet 2026 Player-Centric Ball Action Spotting: Per-Player Attention with Agreement-Based Ensembling [0.0]
  We present our submission to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge.<n>A Track-Aware Action Detector (TAAD) produces per-player action logits from broadcast video, and a Denoising Sequence Transduction (DST) transformer converts game-state features and TAAD logits into structured event sequences.<n>For the DST stage, we introduce a two-stage per-player attention mechanism operating on game-state features, and show that a spatial-first attention ordering improves validation Macro-F1 by 1.87%.
  arXiv  Detail & Related papers  (2026-06-23T09:16:04Z)
• SoccerNet 2025 Challenges Results [205.71032061537747]
  SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open effort, dedicated to advancing computer vision research in football video understanding.<n>This year's challenges span four vision-based tasks: Team Ball Action Spotting, Monocular Depth Estimation, Multi-View Foul Recognition, and Game State Reconstruction.<n>Report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community.
  arXiv  Detail & Related papers  (2025-08-26T16:37:07Z)
• From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction [0.19748373512880277]
  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.
  arXiv  Detail & Related papers  (2025-04-08T18:10:44Z)
• 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)
• The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report [180.94772271910315]
  This paper reviews the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs. The challenge had 262 registered participants, and 34 teams made valid submissions.
  arXiv  Detail & Related papers  (2024-04-16T07:26:20Z)
• SoccerNet 2023 Tracking Challenge -- 3rd place MOT4MOT Team Technical Report [0.552480439325792]
  The SoccerNet 2023 tracking challenge requires the detection and tracking of soccer players and the ball. We employ a state-of-the-art online multi-object tracker and a contemporary object detector for player tracking. Our method achieves 3rd place on the SoccerNet 2023 tracking challenge with a HOTA score of 66.27.
  arXiv  Detail & Related papers  (2023-08-31T11:51:16Z)
• Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset [13.502952342104644]
  We provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023.
  arXiv  Detail & Related papers  (2023-06-27T17:57:34Z)
• 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)
• Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track [78.64815984927425]
  The goal of weakly-supervised temporal action localization is to temporally locate and classify action of interest in untrimmed videos. We adopt the two-stream consensus network (TSCN) as the main framework in this challenge. Our solution ranked 2rd in this challenge, and we hope our method can serve as a baseline for future academic research.
  arXiv  Detail & Related papers  (2021-06-21T03:36:36Z)

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.