超越像素：利用足球的语言来提升广播视频中的时空动作检测
Beyond Pixels: Leveraging the Language of Soccer to Improve Spatio-Temporal Action Detection in Broadcast Videos
AI总结：
本文提出通过引入去噪序列转换任务，利用足球战术规律提升广播视频中时空动作检测的精度和召回率。
摘要：
最先进的时空动作检测（STAD）方法在提取足球赛事事件方面表现出有希望的结果。然而，当在需要全面覆盖赛事事件的高召回率、低精确度模式下运行时，其缺乏上下文理解的局限性变得明显：许多假阳性可以通过考虑更长的动作序列和比赛状态信息来解决。在本工作中，我们通过在游戏层面进行推理并添加去噪序列转换任务来解决这一限制。通过使用基于Transformer的编码器-解码器模型，将噪声的、无上下文的球员中心预测序列与干净的比赛状态信息一起处理。通过建模扩展的时间上下文并在团队层面动态上联合推理，我们的方法利用足球的
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
