In this work, we present a novel Sports Ball Detection and Tracking (SBDT) method that can be applied to various sports categories. Our approach is composed of (1) high-resolution feature extraction, (2) position-aware model training, and (3) inference considering temporal consistency, all of which are put together as a new SBDT baseline. Besides, to validate the wide-applicability of our approach, we compare our baseline with 6 state-of-the-art SBDT methods on 5 datasets from different sports categories. We achieve this by newly introducing two SBDT datasets, providing new ball annotations for two datasets, and re-implementing all the methods to ease extensive comparison. Experimental results demonstrate that our approach is substantially superior to existing methods on all the sports categories covered by the datasets. We believe our proposed method can play as a Widely Applicable Strong Baseline (WASB) of SBDT, and our datasets and codebase will promote future SBDT research. Datasets and codes are available at https://github.com/nttcom/WASB-SBDT .

翻译：本文提出一种新颖的运动球类检测与跟踪（SBDT）方法，可适用于多种运动类别。该方法由以下三部分构成：（1）高分辨率特征提取，（2）位置感知模型训练，（3）考虑时序一致性的推理过程，三者共同构成新的SBDT基线。此外，为验证方法的广泛适用性，我们将所提基线方法在来自不同运动类别的5个数据集上与6种当前最优SBDT方法进行了对比。为此，我们新引入了两个SBDT数据集，为两个现有数据集补充了球体标注，并重新实现了所有对比方法以简化大规模比较。实验结果表明，本方法在数据集涵盖的所有运动类别上均显著优于现有方法。我们相信所提方法可作为SBDT领域的广泛适用强基线（WASB），而数据集与代码库将推动未来SBDT研究。数据集与代码已开源至 https://github.com/nttcom/WASB-SBDT 。