  One of the requirements for team sports analysis is to track and recognize
players. Many tracking and reidentification methods have been proposed in the
context of video surveillance. They show very convincing results when tested on
public datasets such as the MOT challenge. However, the performance of these
methods are not as satisfactory when applied to player tracking. Indeed, in
addition to moving very quickly and often being occluded, the players wear the
same jersey, which makes the task of reidentification very complex. Some recent
tracking methods have been developed more specifically for the team sport
context. Due to the lack of public data, these methods use private datasets
that make impossible a comparison with them. In this paper, we propose a new
generic method to track team sport players during a full game thanks to few
human annotations collected via a semi-interactive system. Non-ambiguous
tracklets and their appearance features are automatically generated with a
detection and a reidentification network both pre-trained on public datasets.
Then an incremental learning mechanism trains a Transformer to classify
identities using few game-specific human annotations. Finally, tracklets are
linked by an association algorithm. We demonstrate the efficiency of our
approach on a challenging rugby sevens dataset. To overcome the lack of public
sports tracking dataset, we publicly release this dataset at
https://kalisteo.cea.fr/index.php/free-resources/. We also show that our method
is able to track rugby sevens players during a full match, if they are
observable at a minimal resolution, with the annotation of only 6 few seconds
length tracklets per player.
