  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.
