SoccerNet 2026 Player-Centric Ball Action Spotting: Per-Player Attention with Agreement-Based Ensembling
We present a two-stage pipeline for player-centric ball action spotting in soccer videos. A Track-Aware Action Detector (TAAD) with temporal transformer produces per-player action logits, and a Denoising Sequence Transduction (DST) transformer converts game-state features and TAAD logits into structured events. Spatial-first attention ordering improves Macro-F1 by 1.87%. A weighted ensemble with agreement filtering raises Macro-F1 from 48.6 to 58.94 on the challenge.
[2606.28389] SoccerNet 2026 Player-Centric Ball Action Spotting: Per-Player Attention with Agreement-Based Ensembling
[Submitted on 23 Jun 2026]
Title:SoccerNet 2026 Player-Centric Ball Action Spotting: Per-Player Attention with Agreement-Based Ensembling
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Abstract:We present our submission to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge, which uses a two-stage pipeline: 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. We improve the TAAD with a temporal transformer that adds cross-frame context, alongside several training fixes. 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 (cross-player attention before temporal attention) improves validation Macro-F1 by 1.87%. To exploit architectural diversity, we train four model variants and combine them with a Weighted Event Fusion ensemble that applies agreement filtering to suppress single-model false positives while preserving recall, plus a dedicated exception for the rare tackle class. Our final system improves the challenge Macro-F1 from a baseline of 48.6 to 58.94.
Comments: 2 pages, 1 figure, 2 tables. SoccerNet 2026 challenge technical report
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.28389 [cs.CV]
(or arXiv:2606.28389v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.28389
arXiv-issued DOI via DataCite
Submission history
From: Faisal Altawijri [view email] [v1] Tue, 23 Jun 2026 09:16:04 UTC (5 KB)
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