Tracking objects in soccer videos is extremely important to gather both
player and team statistics, whether it is to estimate the total distance run,
the ball possession or the team formation. Video processing can help automating
the extraction of those information, without the need of any invasive sensor,
hence applicable to any team on any stadium. Yet, the availability of datasets
to train learnable models and benchmarks to evaluate methods on a common
testbed is very limited. In this work, we propose a novel dataset for multiple
object tracking composed of 200 sequences of 30s each, representative of
challenging soccer scenarios, and a complete 45-minutes half-time for long-term
tracking. The dataset is fully annotated with bounding boxes and tracklet IDs,
enabling the training of MOT baselines in the soccer domain and a full
benchmarking of those methods on our segregated challenge sets. Our analysis
shows that multiple player, referee and ball tracking in soccer videos is far
from being solved, with several improvement required in case of fast motion or
in scenarios of severe occlusion.

在本篇论文中，我们提出了一个可用于多物体跟踪的新型数据集，其包含 200 个 30 秒的序列和一个完整的 45 分钟半场，用于跟踪足球视频中的球员、裁判和球，该数据集带有边界框和跟踪 ID 的完整注释，并能够训练足球领域 MOT 基线并对这些方法进行全面基准测试，结果表明足球视频中的多人、裁判和球跟踪问题还有待改善。