  In a soccer game, the information provided by detecting and tracking brings
crucial clues to further analyze and understand some tactical aspects of the
game, including individual and team actions. State-of-the-art tracking
algorithms achieve impressive results in scenarios on which they have been
trained for, but they fail in challenging ones such as soccer games. This is
frequently due to the player small relative size and the similar appearance
among players of the same team. Although a straightforward solution would be to
retrain these models by using a more specific dataset, the lack of such
publicly available annotated datasets entails searching for other effective
solutions. In this work, we propose a self-supervised pipeline which is able to
detect and track low-resolution soccer players under different recording
conditions without any need of ground-truth data. Extensive quantitative and
qualitative experimental results are presented evaluating its performance. We
also present a comparison to several state-of-the-art methods showing that both
the proposed detector and the proposed tracker achieve top-tier results, in
particular in the presence of small players.
