arXiv:2607.07320cs.CV2026-07

SoccerNet 2026发布5项足球视频理解挑战,推动体育视觉研究进展。

SoccerNet 2026 Challenges Results

  • 设置5个视觉任务,涵盖动作预测、球员定位、视角合成等
  • 427支队伍提交1129份结果,28篇技术报告被评审
  • 提供标注数据与统一评估标准,适合体育视觉研究者参考

SoccerNet 2026挑战赛是第六届面向体育视频理解的开放基准评测活动。本年度共设五个视觉任务:(1) 球类动作预测,基于前序观察窗口预测未来短时内球相关动作的时间与类别;(2) 球员中心球类动作定位,通过球队归属和球衣号码将动作关联到具体球员;(3) 新视角合成,在多视角足球场景中生成未观测相机视角的图像;(4) Spiideo SoccerNet Synloc,从单个校准静态摄像头图像中定位运动员的真实场地坐标;(5) 视觉问答,基于文本、图像和视频输入回答关于足球转播的多选问题。每项任务均提供标注数据、统一评估协议及公开基线。本届挑战吸引427支队伍参与,提交1129份结果,28支团队提交经评审的技术报告。本文介绍各任务及其评估方式,公布排行榜,并总结领先方案,旨在记录在保留数据上各任务的当前技术水平。

原文摘要 · Abstract (English)

The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video understanding. This year's challenges span five vision-based tasks: (1) Ball Action Anticipation, predicting the timing and class of ball-related actions within a short future window from a preceding observation window; (2) Player-Centric Ball Action Spotting, temporally localizing and classifying ball-related actions while assigning each action to the acting player through team affiliation and jersey number; (3) Novel View Synthesis, rendering images from unobserved camera poses in multi-view football scenes; (4) Spiideo SoccerNet Synloc, localizing athletes in real-world pitch coordinates from a single calibrated static-camera image; and (5) Visual Question Answering, answering multiple-choice questions about football broadcasts across text, image, and video inputs. For each task, participants were provided with annotated data, a unified evaluation protocol, and a public baseline. This edition saw broad participation, with 427 teams submitting 1,129 entries across the five tasks and 28 teams contributing reviewed technical reports. This paper describes each task and its evaluation protocol, presents the challenge leaderboards, and summarizes the leading submissions, with the aim of documenting the current state of each task as measured on held-out challenge data.

体育视觉动作预测多视角合成足球分析

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