Motion blur reduces the clarity of fast-moving objects, posing challenges for detection systems, especially in racket sports, where balls often appear as streaks rather than distinct points. Existing labeling conventions mark the ball at the leading edge of the blur, introducing asymmetry and ignoring valuable motion cues correlated with velocity. This paper introduces a new labeling strategy that places the ball at the center of the blur streak and explicitly annotates blur attributes. Using this convention, we release a new table tennis ball detection dataset. We demonstrate that this labeling approach consistently enhances detection performance across various models. Furthermore, we introduce BlurBall, a model that jointly estimates ball position and motion blur attributes. By incorporating attention mechanisms such as Squeeze-and-Excitation over multi-frame inputs, we achieve state-of-the-art results in ball detection. Leveraging blur not only improves detection accuracy but also enables more reliable trajectory prediction, benefiting real-time sports analytics.

翻译：运动模糊会降低快速移动物体的清晰度，给检测系统带来挑战，尤其在球拍类运动中，球体常呈现为条纹状而非清晰点。现有的标注惯例将球体标记在模糊条纹的前沿，这种做法引入了不对称性，并忽略了与速度相关的宝贵运动线索。本文提出一种新的标注策略，将球体置于模糊条纹的中心，并明确标注模糊属性。基于此惯例，我们发布了一个新的乒乓球检测数据集。实验表明，这种标注方法能持续提升多种模型的检测性能。此外，我们提出了BlurBall模型，该模型能够联合估计球体位置与运动模糊属性。通过在多帧输入上引入注意力机制（如Squeeze-and-Excitation），我们在球体检测任务中取得了最先进的结果。利用模糊信息不仅提高了检测精度，还实现了更可靠的轨迹预测，从而有益于实时体育数据分析。