Towards Active Learning for Action Spotting in Association Football Videos

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.

Automatic event detection in football using tracking data

One of the main shortcomings of event data in football, which has been extensively used for analytics in the recent years, is that it still requires manual collection, thus limiting its availability to a reduced number of tournaments. In this work, we propose a computational framework to automatically extract football events using tracking data, namely the coordinates of all players and the ball. Our approach consists of two models: (1) the possession model evaluates which player was in possession of the ball at each time, as well as the distinct player configurations in the time intervals where the ball is not in play; (2) the event detection model relies on the changes in ball possession to determine in-game events, namely passes, shots, crosses, saves, receptions and interceptions, as well as set pieces. First, analyze the accuracy of tracking data for determining ball possession, as well as the accuracy of the time annotations for the manually collected events. Then, we benchmark the auto-detected events with a dataset of manually annotated events to show that in most categories the proposed method achieves $+90\%$ detection rate. Lastly, we demonstrate how the contextual information offered by tracking data can be leveraged to increase the granularity of auto-detected events, and exhibit how the proposed framework may be used to conduct a myriad of data analyses in football.