  Association football is a complex and dynamic sport, with numerous actions
occurring simultaneously in each game. Analyzing football videos is challenging
and requires identifying subtle and diverse spatio-temporal patterns. Despite
recent advances in computer vision, current algorithms still face significant
challenges when learning from limited annotated data, lowering their
performance in detecting these patterns. In this paper, we propose an active
learning framework that selects the most informative video samples to be
annotated next, thus drastically reducing the annotation effort and
accelerating the training of action spotting models to reach the highest
accuracy at a faster pace. Our approach leverages the notion of uncertainty
sampling to select the most challenging video clips to train on next, hastening
the learning process of the algorithm. We demonstrate that our proposed active
learning framework effectively reduces the required training data for accurate
action spotting in football videos. We achieve similar performances for action
spotting with NetVLAD++ on SoccerNet-v2, using only one-third of the dataset,
indicating significant capabilities for reducing annotation time and improving
data efficiency. We further validate our approach on two new datasets that
focus on temporally localizing actions of headers and passes, proving its
effectiveness across different action semantics in football. We believe our
active learning framework for action spotting would support further
applications of action spotting algorithms and accelerate annotation campaigns
in the sports domain.
