To build a smart basketball court, one basic task is to track players with the aid of the basketball court monitoring. This task can be regarded as a special case of the multiple object tracking (MOT) problem. But different from it, the task puts request to the existing MOT methods with both good accuracy and operational efficiency (toward real time) in the new basketball scenario. To deal with this task, we make the following attempts: (1) Considering the differences between pedestrians and basketball players and the lack of corresponding dataset for basketball players tracking, we construct a new MOT dataset under the basketball court monitoring scene, to better evaluate MOT methods in our task and to help promote future research in the related task, (2) evaluating the performance of the several candidate MOT methods on the new dataset, and (3) proposing the issues to be further addressed in this specific scenario.

建立一个智能篮球场，一项基本任务是借助篮球场监控来跟踪运动员。此任务可以视为多对象跟踪（MOT）问题的特例。但与此不同的是，该任务在新的篮球场景中提出了对现有MOT方法的要求，该方法具有良好的准确性和操作效率（实时性）。为解决这一问题，我们做了以下尝试：（1）考虑到行人与篮球运动员之间的差异以及缺少用于跟踪篮球运动员的相应数据集，我们在篮球场监控场景下构建了一个新的MOT数据集，以更好地评估MOT方法在我们的任务中，并有助于促进相关任务的未来研究，（2）在新数据集上评估几种候选MOT方法的性能，