A Methodology for Real-Time Data Verification exploiting Deep Learning and Model Checking

This paper presents a methodology for real-time data extraction and verification. In particular, considering the lacking of real-time data in sport analytics context, we propose a method to generate a data-set of player positions from soccer game videos, considering deep learning techniques, in order to extract player position, in terms of x-axis and y-axis, related to the accuracy of the detection. The experiment is performed on several local soccer games, captured using a stationary camera situated on the tribune area close to the center of the field. The camera is positioned in order to cover the entire soccer field. Clearly, this methodology can allow us to extract other types of information, such as distance between players and ball, and the area covered by all the players in a specific situation of the game. All of the informations are stored in a CSV (comma separated value) file that can be used to verify behavioural properties exploiting formal methods.