用足球战术语言提升视频动作检测的准确率
Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos
- 引入足球比赛语境与团队动态建模，优化动作预测
- 在低置信度场景下同时提升精确率与召回率
- 适合需要高覆盖性足球分析的场景
当前最先进的时空动作检测（STAD）方法在从广播视频中提取足球事件方面表现良好。但在需全面覆盖赛事事件的高召回、低精度模式下，其缺乏上下文理解能力导致大量误报。本文通过在比赛层面进行推理，引入去噪序列转换任务，将噪声大、无上下文的以球员为中心的预测与清晰的比赛状态信息结合，使用基于Transformer的编码器-解码器模型联合建模长时间依赖关系和团队级动态。该方法利用足球的战术规律与球员间依赖关系，生成更准确的动作序列。实验表明，该方法在低置信度情况下显著提升精度与召回率，实现更可靠的事件提取，可有效补充现有基于像素的方法。
原文摘要 · Abstract (English)
State-of-the-art spatio-temporal action detection (STAD) methods show promising results for extracting soccer events from broadcast videos. However, when operated in the high-recall, low-precision regime required for exhaustive event coverage in soccer analytics, their lack of contextual understanding becomes apparent: many false positives could be resolved by considering a broader sequence of actions and game-state information. In this work, we address this limitation by reasoning at the game level and improving STAD through the addition of a denoising sequence transduction task. Sequences of noisy, context-free player-centric predictions are processed alongside clean game state information using a Transformer-based encoder-decoder model. By modeling extended temporal context and reasoning jointly over team-level dynamics, our method leverages the "language of soccer" - its tactical regularities and inter-player dependencies - to generate "denoised" sequences of actions. This approach improves both precision and recall in low-confidence regimes, enabling more reliable event extraction from broadcast video and complementing existing pixel-based methods.
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