新标注方式+联合估计算法，提升乒乓球追踪精度与轨迹预测。
BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking
- 将球心置于模糊中心，显式标注模糊属性
- 在多个模型上实现检测性能提升，轨迹预测更可靠
- 适合实时体育分析与高速运动目标追踪场景
运动模糊会降低快速移动物体的清晰度，对检测系统构成挑战，尤其在网球等球拍类运动中，球体常呈现为条纹而非明确点状。现有标注习惯将球标于模糊前端，引入不对称性并忽略与速度相关的运动线索。本文提出新标注策略：将球置于模糊条纹中心，并显式标注模糊属性。基于此，我们发布了一个新的乒乓球检测数据集。实验表明，该标注方式在多种模型上均能持续提升检测性能。此外，我们提出BlurBall模型，可联合估计球位置与运动模糊属性。通过在多帧输入中引入Squeeze-and-Excitation注意力机制，实现当前最佳检测效果。利用模糊信息不仅提升检测准确率，还增强轨迹预测可靠性，有助于实时体育数据分析。
原文摘要 · Abstract (English)
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.
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